This paper presents an AC-DC converter system tailored for grid-to-vehicle (G2V) applications, aimed at facilitating efficient power flow while achieving a Unity power factor (UPF). The system employs a rectifier for AC-DC conversion, which effectively steps up a 230V AC input to a 380V DC output. This DC output can be further regulated using a buck converter to meet specific load requirements. A Proportional-Integral (PI) controller is implemented to oversee the voltage and current regulation, thereby minimizing harmonic distortion and enhancing the overall power factor. By actively managing the input voltage and current, the controller ensures that the system operates within desired parameters, thus optimizing power quality. Comprehensive simulation results validate the system's performance, demonstrating its capability to maintain a UPF in G2V mode. The findings indicate significant reductions in total harmonic distortion (THD), reinforcing the system's effectiveness in managing power quality. This AC-DC converter design not only enhances the efficiency of power flow in electric vehicle charging systems but also contributes to the stability of the grid by minimizing reactive power and harmonics. Overall, this work represents a significant advancement in converter technology for sustainable transportation and energy management.

In this paper, A new customized multi-level inverter (MLI) configuration is proposed for induction motor drive, aiming to lower the requirement of DC bus voltage magnitude. This method utilizes pole pair winding coils separately to generate multi-level voltage waveform across the total stator phase windings. As the inverter requires lower input voltage it eliminates the requirement of boost converters when it is used in the EV applications. The inherent advantages of this topology significantly reduce control complexity in the battery systems by reducing the number of series-connected battery cells. The conventional Level-Shifted Sine Triangle PWM technique proficiently shifts low-frequency harmonics to the carrier frequency, enhancing power quality and minimizing electromagnetic interference. Through MATLAB simulation, this new customized multi-level inverter-fed open-end stator winding Induction motor is simulated and results are presented to validate the proposed concept. Ultimately, our research aims to contribute to advancing electric vehicle technology by operating the induction motor with minimal input DC source voltage, and substantial output gain.

This paper introduces a new leg-integrated switched capacitor inverter (LISCI) structure for efficient three-phase induction motor operations powered by solar panels. Traditional inverter configurations often face challenges related to efficiency, size, and cost. The presented LISCI structure addresses these issues by integrating switched capacitor networks directly within the inverter legs, offering significant improvements in performance and compactness. Key features of the LISCI structure include reduced component count, enhanced voltage gain, and improved harmonic performance. The inverter's innovative design enables it to achieve higher efficiency by minimizing switching losses and optimizing power distribution. Additionally, the integrated capacitors contribute to a more stable voltage output, critical for the reliable operation of three-phase induction motors.

The rising demand for sustainable transportation has sparked significant interest in solar-powered electric vehicles (EVs). However, integrating solar energy into EV drivetrains, particularly those using Permanent Magnet Synchronous Motors (PMSMs), presents challenges due to the occasional nature of solar power needed for consistent vehicle performance under varying environmental conditions. This paper introduces a high-performance solar-fed PMSM system for electric vehicles, incorporating advanced control techniques and an intelligent energy management strategy (EMS). The system employs Field-Oriented Control (FOC) for precise motor speed regulation and a Fuzzy Logic-based Maximum Power Point Tracking (MPPT) algorithm to optimize solar energy harvesting. A lithium-ion battery serves for efficient energy storage, enabling the system to store and use solar power effectively. The EMS dynamically allocates energy between the solar panels, battery, and motor, maximizing energy efficiency and extending the vehicle's range. The system was tested in MATLAB/Simulink simulations and validated using dSPACE DS1104 hardware for real-time control. The simulation results, coupled with hardware testing, demonstrate improved energy efficiency and reduced reliance on external charging sources. These findings position solar-powered EVs as a competitive and sustainable solution for the future, offering significant benefits to industries in EV manufacturing and renewable energy. The integration of solar power not only enhances sustainability but also addresses the growing demand for green and efficient transportation.

Accurate wind power forecasting is essential for the effective integration of wind energy into power grids. Yet, the inherent variability of wind and the intricate interplay of meteorological factors make prediction a challenging task. This study introduces a novel short-term wind power forecasting method, improving the traditional convolutional neural network and long short-term memory (CNN-LSTM) model through two significant innovations. First, we introduce a parallel convolutional architecture that employs both 1dimensional (1D) and 2-dimensional (2D) convolutions to simultaneously capture temporal patterns and inter-variable relationships in wind power data. This structure, inspired by Explainable-CNNs, enables more comprehensive feature extraction. Second, we integrate an attention mechanism that dynamically weights the importance of different input features and time steps, improving both forecast accuracy and model interpretability. The proposed model is evaluated using data from two wind farms in Croatia, comparing its performance against benchmark models including standard CNN-LSTM, LSTM, and gated recurrent unit (GRU) networks. Results demonstrate that our enhanced CNN-LSTM model achieves superior forecasting accuracy, with improvements in Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of 15% and 12% respectively, compared to the best-performing benchmark. Furthermore, the attention mechanism provides valuable insights into the relative importance of different features over time, offering a new level of interpretability in wind power forecasting models. This work contributes to the advancement of accurate and explainable wind power prediction, supporting more efficient renewable energy integration and grid management.

The urgent need for high-performance, sustainable electric vehicle (EV) batteries has driven the exploration of advanced computational methods to accelerate material discovery. Traditional approaches, such as Density Functional Theory (DFT) and Hartree-Fock, face inherent limitations in simulating the complex quantum behaviors of novel battery materials. This paper introduces a pioneering framework leveraging quantum computing, specifically the Variational Quantum Eigen solver (VQE), to overcome these challenges and optimize solid-state battery materials. We focus on Lithium Thiophosphate (Li3PS44), a promising electrolyte for next-generation batteries, and demonstrate how quantum simulations can provide a deeper understanding of electronic structures and electrochemical reactions at an unprecedented level of precision. By benchmarking quantum results against classical methods, we highlight the transformative potential of quantum algorithms to capture intricate electron correlations and reaction dynamics, offering more accurate predictions for material performance. Our findings suggest that quantum computing not only offers a significant leap in the accuracy of battery material simulations but also paves the way for scalable, data-driven optimization of next-generation energy storage systems.

The rapid rise in electric vehicle (EV) adoption highlights the critical need for a reliable charging infrastructure to ensure the stability of power distribution networks. This research introduces a fuzzy inference system (FIS) designed to forecast daily EV loads essential for developing microgrids to meet the increasing demand for EVs. The present work considers four factors for FIS designing: travel distance, parking duration, battery state of charge (SoC), and expected arrival times at charging stations. By developing fuzzy logic rules for these variables, a probabilistic charging is generated, improving both the precision and adaptability of load forecasts. This study also explores the impact of future EV adoption on microgrid load demand, analyzing adoption rates of 53%, 68%, and 84%, providing crucial insights for planning microgrids. The discrepancy between estimated and actual EV loads is found to be 0.078, demonstrating a reduction in prediction error. This effectively mitigates uncertainties related to EV user behavior and supports the design of resilient and flexible microgrid systems.

This paper presents an optimized control strategy for a Brushless DC (BLDC) motor driven by a photovoltaic (PV) system, incorporating Maximum Power Point Tracking (MPPT) using the Perturb and Observe (P&O) method, Field-Oriented Control (FOC), and battery storage. The Proportional-Integral (PI) controller for motor speed regulation is optimized using the Bat Algorithm (BA), improving performance metrics such as settling time, steady-state error, rise time, and overshoot. Hall Effect sensors provide accurate rotor position and speed feedback, enabling precise commutation and control. The MPPT algorithm ensures maximum power extraction from the PV panel under varying sunlight conditions, while a DC-DC boost converter increases the voltage. to the necessary level for the BLDC motor. The battery storage system ensures continuous operation during periods of low solar input. Simulation results indicate that this design effectively harnesses solar energy, providing stable motor operation under changing load and irradiance conditions. It is well-suited for applications such as electric vehicles, water pumping systems, and robotics, offering a sustainable off-grid power solution for BLDC motor-driven systems.

Due to environmental concerns, renewable energy has gained significant popularity over the past two decades. Integrating distributed generation and renewable energy sources, particularly through microgrids in power distribution systems, has become feasible. Additionally, there has been a notable increase in the adoption of electric vehicles (EVs) driven by environmental initiatives and their advantages over internal combustion engines. As a result, the planning and operation of microgrids in distribution systems have become more complex. To address these complexities, computational evolutionary algorithms have emerged as effective solutions. The Differential Evolution (DE) algorithm stands out for its speed and user-friendly simplicity The proposed study uses the Fuzzy Adaptive Differential Evolution (FADE) analysis for microgrid planning integrated with EV charging infrastructure, using the IEEE 33-bus system. The FADE algorithm combines the power of fuzzy logic and adaptive strategies within the DE framework to tackle the planning and optimization challenges of microgrids integrated with Electric Vehicle Charging Stations (EVCS) The findings provide valuable insights into the effectiveness of the FADE algorithm in addressing the challenges associated with the planning and operation of microgrids with EVCS in modern power systems.

This study examines the integration of permanent magnet synchronous motors (PMSM) with renewable energy sources, focusing on solar photovoltaic (SPV) arrays to improve efficiency and sustainability in electric vehicle (EV) applications. PMSM, renowned for its high efficiency, silent operation, and precise control, is managed using a proportional-integral (PI) controller to handle variable load conditions, including fluctuations in torque and current. By fine-tuning the PI controller's gains, the desired motor speed is achieved efficiently. A DC-DC Buck-Boost converter serves as an intermediary power conditioning unit, optimizing energy extraction from the SPV array and enhancing system efficiency. This setup ensures that PMSM meets the power and operational demands of EVs. Additionally, a voltage source inverter (VSI) facilitates electronic commutation of the PMSM, providing accurate control using fundamental frequency pulses. The system is modelled and simulated in MATLAB/Simulink, demonstrating its reliability under diverse load conditions. The findings underscore the potential of this approach in promoting renewable energy integration in EVs, paving the way for cleaner and more sustainable transportation solutions.

MPPT is crucial for optimizing the efficiency of PV systems. However, conventional methods such as Perturb and Observe (P&O) and Incremental Conductance (I&C) suffer from slow convergence, steady-state oscillations, and failure to track the global maximum power point (GMPP) under partial shading conditions (PSC). To address these limitations, this paper proposes a hybrid MPPT strategy integrating Incremental Conductance (I&C) and spider Monkey Optimization (SMO). The I&C method ensures rapid tracking under uniform irradiance, while the SMO algorithm is activated under PSC to identify the true GMPP, overcoming local maxima issues. The extracted power is regulated using a boost converter to charge a battery, which supplies an inverter-fed Brushless DC (BLDC) motor. A closed-loop PI controller with an adaptive mechanism ensures precise speed control, minimizing torque ripples and enhancing system stability. Simulation results validate the proposed approach, demonstrating higher MPPT efficiency, reduced power loss, and improved motor performance under dynamic conditions. The proposed system enhances the reliability of solar-powered BLDC motor drives, making it a viable solution for electric vehicle and industrial automation applications.Several hybrid MPPT strategies have been explored in literature, including combinations of I&C withHybrid MPPT strategies have been explored, combining I&C with Particle Swarm Optimization (PSO), Genetic Algorithms, and Grey Wolf Optimization (GWO), each addressing various trade-offs between speed and global accuracy. Compared to these, SMO offers a better balance of exploration and convergence control.

Electric vehicle (EV) charging systems face critical challenges, including energy conversion inefficiencies, high voltage and current ripple, and instability under fluctuating grid conditions. These issues not only degrade performance but also shorten the lifespan of components and accelerate battery wear. Traditional charging systems often fail to address power quality concerns such as poor power factor and high total harmonic distortion (THD), which further exacerbate inefficiencies and negatively impact grid integration. This paper proposes a groundbreaking solution to these challenges by integrating a highefficiency single-phase AC-to-DC interleaved buck-boost converter with Adaptive Predictive PI-Fuzzy Power Factor Control (AP-PFC). By reducing ripple, improving thermal management, and optimizing power stage balancing, the interleaved buckboost converter significantly enhances efficiency and extends the lifespan of system components. The PFC stage ensures nearunity power factor, minimizes THD, and guarantees seamless grid integration. The hybrid PI-fuzzy controller adds real-time adaptability by dynamically adjusting PI gains based on key parameters like temperature, grid voltage, and battery state of charge (SOC), ensuring optimal performance under varying conditions. Furthermore, the MPC algorithm anticipates future system behavior, reducing energy losses and charging time while safeguarding battery health. Simulation results highlight the significant improvements in ripple reduction, charging efficiency, and battery longevity offered by the proposed system. This innovative approach presents a scalable, reliable, and adaptable solution that not only maximizes energy efficiency but also guarantees fast, secure, and sustainable EV charging, making it ideal for dynamic grid environments and next-generation EV charging infrastructure.

Sensorless control of Brushless DC (BLDC) motors is a cost-effective and reliable alternative to traditional Hall sensor-based methods, eliminating the need for additional hardware while enhancing system robustness. This study integrates a proportional-integral (PI) controller with a robust closed-loop sensorless speed control strategy for a BLDC motor. Back-EMF Zero-Crossing Detection (ZCD). By introducing a 30° phase delay for exact commutation and collecting rotor position information from the back-EMF of the unexcited phase, the suggested method eliminates the need for position sensors. By dynamically modifying the PulseWidthModulation (PWM) duty cycle of the VoltageSource Inverter (VSI) based on real-time speed error, an API controller is built to control motor speed. MATLAB/Simulink is used to model and simulate the system, which consists of a BLDC motor, VSI, DClink capacitor, and AC rectifier. Real-time implementation using dSPACE further validates the suggested control strategy by demonstrating stable speed control, fast dynamic response, and decreased steady-state error. The sensorless control method provides a cost-effective, efficient, and reliable solution, making it highly suitable for industrial automation, electric vehicles, and renewable energy applications.

This work presents a novel multi-port high-gain bidirectional DC-DC converter (MPHG-BDC) designed for energy storage systems with consumer benefits. The proposed MPHG-BDC enables the integration of multiple low voltage sources, utilizing modular converters to achieve high step-up gain in boost mode and high step-down gain in buck mode through the voltage multiplier. Thus, facilitating the grid to vehicle (G2V) and vehicle to grid (V2G) power flow, which the consumers can utilize the electric vehicular batteries as a backup power supply. Thus, facilitates power availability even in remote areas for household electrification. The highlights include reduced averaged normalized switch voltage stress, continuous LV currents, multiple low-voltage source integration, and ease of control. The proposed MPHG-BDC is thoroughly analyzed under steady-state conditions, with and without accounting for the non-idealities. A detailed examination of the boost and the buck modes, loss analysis, and comparison with existing bidirectional converter topologies are provided to showcase the performance of the proposed converter. The overall efficiency of converter is analyzed and discussed. At rated conditions, the efficiency in the boost mode is 93.2% and in buck mode is 92.7%. The operation with independent source operation (failure mode case) is verified and results are presented. The theoretical aspects are validated using a 100W laboratory module.

This paper provides a detailed examination of speed control methods for induction motors, with a specific focus on the use of different pulse width modulation (PWM) techniques to achieve precise speed regulation and efficient motor operation. The study investigates the application of sinusoidal PWM (SPWM), third harmonic injection PWM (THPWM), space vector PWM (SVPWM), and selective harmonic elimination PWM (SHEPWM). The proposed hybrid PWM technique is analyzed and compared with existing PWM techniques in both open-loop and closed-loop control strategies. The incorporation of feedback mechanisms such as speed sensors to dynamically adjust the PWM signals has been considered. Through the adjustment of carrier signal frequency and modulation index, the study identifies the optimal PWM technique for minimizing total harmonic distortion (THD) and switching losses. The paper concludes with recommendations on the most effective PWM techniques for specific conditions.

A novel non-isolated High-Gain DC-DC Converter with Moderate Gain for Hybrid Energy System applications on DC Microgrids. The paper proposes a novel high-gain DC-DC converter for Hybrid energy systems such as Solar Photovoltaic (PV) systems, Fuel cells (FC), etc. The converter can replace the necessity of multiple converters for multiple sources. The major contributions are the lower switch voltage stress, High boost gain, multiple input capability, and lower component count as a dual source capability. The design and analysis of ideal and non-ideal conditions of the components are discussed and the individual effects of each component are analyzed. Further, the non-ideal gain and non-ideal efficiency are derived and presented. Also, Simulation results with a rated power of 100W are presented.

This paper introduces novel high-gain tertiary port boost converter (HGTPBC) designed for hybrid energy sources such as solar photovoltaic (PV) and fuel cells (FC). The converter is employed with dual input sources by facilitating modular converters and accomplishes a high step-up voltage gain by virtue of a voltage multiplier in a DC microgrid, where the prosumers can have an islanded operation. The proposed topology allows home appliances to be powered by multiple energy source without the need for a large storage unit. Key features include continuous input current, reduced normalized voltage stress on switches, expandability for multiple input sources and independent source control. The independent control facilitates the standalone operation with single source during source failure or absence. To evaluate the converter performance, a thorough steady-state analysis, both with and without consideration of nonidealities is carried out. Detailed comparisons with existing converter topologies highlight the advantages of the proposed converter. Moreover, the loss distribution and efficiency analysis of proposed converter are presented and found to be 91.59% efficiency at rated power. Theoretical aspects are validated through hardware testing on a 100W laboratory prototype.

The research presents a novel Bidirectional Switched Capacitor DC-DC (BSCD) Converter and demonstrates its application in integrating a battery with an electric vehicle's (EV) traction motor. During discharging, the motor is powered by the battery through the converter, and during charging, the traction motor functions as a generator, returning the recovered energy to the battery via the converter. The recommended converter employs a two-duty cycle operation to enhance voltage gain while minimizing circuit components. It utilizes a switched capacitor (SC) cell, enhancing the voltage transfer ratio by operating capacitors CS1 and CS2 in parallel or series. The work includes analysis of the converter's steady state, mathematical approach, state-space modelling, stability, and efficiency. The proposed converter achieves an efficiency of 90.66 % in charging mode and 96.6 % in discharging mode, with a Gain Margin of 54.4 dB and Phase Margin of 8.09°, indicating stability. Comparative evaluations with existing BDCs are also provided. The implementation of a closed-loop simulation using MATLAB/Simulink and dSpace software validates the performance of the suggested converter-based drive. Furthermore, an experimental investigation of a 200 W, 30 V/430 V configuration confirms the converter's practical viability.

Over the past 20 years, the popularity of renewable energy has sharply increased due to environmental concerns. Integrating Distributed Generation (DG) and renewable energy sources, particularly through microgrids, into power distribution systems has become increasingly feasible. Simultaneously, there has been a notable surge in the adoption of electric vehicles (EVs), driven by environmental initiatives and their advantages over internal combustion engines. Consequently, the planning and management of microgrids within distribution networks have grown increasingly complex. To tackle these complexities, computational evolutionary algorithms have emerged as effective solutions. Among these algorithms, the Differential Evolution (DE) algorithm stands out for its speed and user-friendly simplicity. The proposed work analyzes Hybrid Differential Evolution (HDE) integrated with EV charging infrastructure for microgrid planning. The HDE algorithm combines the power of fuzzy logic and adaptive strategies within the DE framework to address the planning and optimization challenges of microgrids integrated with Electric Vehicle Charging Stations (EVCS). The paper gives insights into the effectiveness of the HDE algorithm in addressing the challenges related to the planning and operation of microgrids with EV charging stations in modern power systems. Furthermore, the optimization results are compared with those achieved using the DE algorithm.

A new non-isolated single-input dual-output (NI-SIDO) DC-DC converter is proposed in this paper. The converter has the advantage of incorporating multiple outputs for energy storage applications, applicable in DC micro-grid storage systems, Electric vehicular charging stations, battery converters, and renewable energy systems without a filter capacitor. The significant advantage of the converter is it uses the interleaving technique to incorporate the outputs. The voltage stress across the switches and capacitor voltage stress is also reduced drastically. Thus it reduces the capacitor size when compared with the conventional boost converter. A closed-loop control strategy is implemented to control the load voltage as well as the inductor current. The converter is designed, analyzed, implemented, and tested using MATLAB SIMULINK software for 150W. The Simulation results are presented under various operating conditions such as changes in load with solar PV systems. The results from real-time testing are presented with the OPAL-RT system.

This paper proposes BKY converter, which is made to run in continuous conduction mode during both the charging and discharging cycles for low power EV applications. An analysis is conducted on the converter's dynamic behavior, and an approach to control is put forth to manage the power transfer between the traction system and battery in an electric vehicle. The suggested converter is designed using an extracted small-signal model. A significant ripple in the detected current causes switching instability in the current-mode control approaches at low duty ratios. A computation delay occurs when the controller is implemented in the microcontroller. The control algorithm's design takes this into account. A theoretical framework for current and voltage loop gain transfer functions are created using the realistic parameters of a BKY converter. Further, dynamic performance under load variations is explained and validated by simulations.

Effective utilization of renewable energy sources (RES) is with the better management of their fluctuation nature. Employing hybrid energy storage systems (HESS) in line with the RES will improve the power flow equilibrium in the DC microgrids (DC-MG). In this paper, a PI control-based hybrid energy storage system with a Proton exchange membrane (PEM) fuel cell (FC), battery, and a supercapacitor (SC) for increasing the effectiveness of renewable power in the DC-MG is presented. A validation test is conducted for a 100 W DC microgrid system to verify the effectiveness of the proposed model. The MATLAB/SIMULINK software is used to implement the proposed system.

Energy storage systems with a high voltage transfer ratio (VTR) play an important role in integrating modern electric power systems with large-scale renewable energy integration. This article suggests a modified Switched Capacitor non-isolated Bidirectional DC–DC Converter (SCBDC) topology to achieve a high VTR. The presented converter has a simple circuit, simple control, a switched capacitor structure that increases the voltage-gain range, and low-voltage stress on switches, making it suitable for renewable and hybrid energy source electric vehicle applications. Continuous conduction mode is used for the operation principles, steady-state analysis, and extraction of voltage and current equations. Simulation results for the proposed converter were obtained in a MATLAB environment, demonstrating the converter's feasibility.

The increasing focus on environmental sustainability has led to a significant rise in the use of renewable energy within distributed generation systems. Microgrids play a crucial role in facilitating the integration of renewable energy into distribution networks, making effective strategic planning essential for achieving the best financial and environmental results. Advanced software tools for microgrid planning and design, such as HOMER, are vital in this context. HOMER stands out for its ability to incorporate contemporary factors such as demand-side management, generator reliability, and Electric Vehicle Charging Fleets (EVCF). The proposed work investigates the planning process for a campus microgrid that includes EVCF, exploring various renewable energy configurations and tariff options. It offers a thorough assessment of different planning scenarios, emphasizing both the potential benefits and challenges associated with incorporating EVCF into university microgrids. The analysis determined that the optimal sizes for the microgrid components could yield annual energy charge savings of $12,027, annual utility bill savings of $281,905, and a payback period of 5.2 years.

This paper investigates the efficacy of V/f scalar control for a three-phase squirrel cage induction motor (IM) integrated with a proportional-integral (PI) controller and MOSFET-based inverter. The key objective is to achieve robust speed regulation and stability under varying load disturbances. In the present work, two control schemes have been delved (a) the closed-loop approach, offering superior performance but less common in industrial settings, and (b) the widely employed open-loop method. Leveraging MATLAB/Simulink, simulations have been performed to compare the performance of three-level and five-level inverter configurations. To quantify the harmonic content, a comprehensive analysis of total harmonic distortion (THD) has been conducted. The study further incorporates the concept of electric vehicles (EVs), exploring how the proposed control strategy could enhance the performance and efficiency of EV drives.

The interconnection of numerous standalone MGs leads towards the establishment of an isolated MMG system. The MMG system is a complex nonlinear system that creates functioning decline as a result of inadequate dampening under the unanticipated variability in the load demand and the generated power from sources of renewable energy catalogue. Nonlinear nature also comes in the system due to the variations in the system parameter and dynamically altering loading conditions. So, in the present work, a standalone MMG system with two areas system through renewable penetration, the operation of QOHSA aimed at the gain optimisation of MS-PID controller is exploited for limiting variation in power and frequency due to generation and load demand perturbation. The practicality of the considered controller (i.e. MS-PID) is unearthed by comparing the dynamic characteristics of isolated MMG systems along with other controllers like I, PI and PID controllers (i.e. classical controllers). The MS-PID controller configuration sustains the deviation of frequency under ±0.00249 Hz and ±0.0583 Hz for step and random change in load demand, respectively. The sensitivity analysis is executed to present the suitability for the extensive adaptations in the magnitude of MG parameters along with the circumstances of step/random load perturbation.

High efficiency, high voltage transfer ratio (VTR), and low input ripple current is required in any bidirectional DC-DC converter (BDC) that plays a major role in interfacing batteries in applications like dc microgrids and electric vehicles (EVs). For meeting these requirements, a switched capacitor-based BDC is proposed to interface the battery with a propulsion system via DC Link. It has a simple circuit with only a set of switching operations, High VTR, and lesser ripple current on the low voltage (LV) side are advantages of the proposed High Gain Switched-Capacitor Bi-directional DC-DC Converter (SC-BDC) making it appropriate for use in EVs. The steady-state analysis, design consideration of passive components, loss and efficiency analysis are presented. Finally, the proposed High Gain SC-BDC is compared with few of the existing BDC in the literature. The feasibility of the converter was demonstrated by simulating a 200 W converter and validating results produced in a MATLAB environment.

Bidirectional converters have often been used in numerous applications like DC microgrids, renewable energy, hybrid energy storage systems, electric vehicles, etc. The paper proposes a novel multi-port high-gain (NMPHG) bidirectional DC–DC converter that supports DC microgrid (DC-MG) applications. The main contributions of the proposed converter are high step-up/step-down conversion gain, multiple input ports, lower switch voltage stress, and lower component count owing to the single converter with multiple input ports for DC microgrid applications. The detailed operational principle, analysis, and design considerations of proposed NMPHG bidirectional DC–DC converters are discussed. Furthermore, the loss analysis, detailed comparison with similar works, and efficiency analysis with non-modalities during forward power flow (LV to HV) and reverse power flow (HV to LV) modes are presented. The efficiency of the proposed converter is found to be 93.8% in forward power flow and 92.9% in reverse power flow modes at rated power. Finally, a hardware prototype of the proposed NMPHG bidirectional DC–DC converters is implemented with 100 W in FPF mode and 200 W in RPF mode with a TMS320F28335 processor and validated with theoretical counterparts.

Bi-directional DC-DC converters (BDC) are required for power flow regulation between storage devices and DC buses in renewable energy based distributed generation systems. The fundamental requirements of the BDC are simple structure, reduced switching components, a wide range of voltage gain, low voltage stress, high efficiency, and reduced size. There are different BDC topologies for various applications based on their requirements in the literature. Various BDC are categorized according to their impedance networks. Isolated BDC converters are large due to high-frequency transformers and hence used for static energy storage applications whereas non-isolated BDC is lightweight and suitable for dynamic applications like electric vehicles. This paper reviews types of non-isolated BDC topologies. The performance of five non-isolated BDC converters under steady state condition is evaluated by using theoretical analysis. On this basis, suitability of BDC for different applications is discussed. Further advantages and limitations of converters are discussed by using comparative analysis. The optimization of BDC for distributed generation systems from the perspectives of wide voltage gain, low electromagnetic interference, low cost with higher efficiency is identified. Theoretical analysis of the converters is validated by simulating 200W converters in MATLAB Simulink.

In electric vehicles (EVs), the type of electric motor and converter technology have a significant impact on regulating the operational characteristics of the vehicle. Therefore, in this work, the modified bi-directional KY converter (BKYC) is proposed for EV applications. The main contributions of the proposed converter are high step-up/step-down conversion gain, bi-directional power flow, simplified control structure, continuous current, common ground, low volume, and high efficiency. An inductor on either side of the converter ensures continuous current flow and passive components are arranged to operate in series to offer high step-up/step-down conversion. The charging and discharging operations, steady-state analysis, and design process of the proposed converter are discussed in detail and compared with similar bi-directional converter topologies. Further, the efficiency analysis of the proposed converter is presented and found that the efficacy of 95.51 % in charging operation and 96.52 % in discharging operation of operation. The simulations are carried out using MATLAB/Simulink environment. Further, a prototype of a modified bi-directional KY converter is implemented with a TMS320F28335 processor and validated with theoretical and simulation counterparts.

Lithium ion batteries are a promising energy source for electric vehicles due to their high specific energy and power output. Overall system reliability and stability can be improved by effectively planning battery replacement intervals and monitoring their condition. To guarantee the battery system operates safely, steadily, and effectively, it is necessary to accurately assess the state of health (SOH) of the lithium-ion battery. Capacity might be used to anticipate it directly. To improve the accuracy of the SOH estimate, hyperparameter-optimized Gaussian process regression (GPR) is used. Gaussian process models have the advantage of being flexible, stochastic, nonparametric models with uncertainty forecasts, and may have variance around the mean forecast to account for the associated uncertainties in evaluation and forecasting. The lithium-ion battery data set made available by NASA is examined in this article. The outcomes demonstrate its efficacy and demonstrate that the algorithm may be successfully used for battery monitoring and prognostics. Additionally, the prediction for battery health has been improved through the comparison of predictions with various quantities of training data.

DC-DC converters are broadly used in many industries, like electric vehicles, industrial inverters, telecommunications systems, and many others. However, these converters face many challenges when it comes to their performance, particularly when they are used with constant power load (CPL) which has negative-incremental resistance. These loads might lead to instability issues with the converter's output voltage. This manuscript offers a way out to the above stated challenge, a robust nonlinear control approach has been developed. The control strategy is constructed on passivity-based controller (PBC) and employs a nonlinear-disturbance observer (NDO) to increase controller effectiveness against CPL. The PBC ensures system stability by dissipating transient energy and on the other hand, NDO operates in parallel to compensate for disruptions via a feed-forward channel. This method produces high signal stability and quick recovery performance during load disturbances and uncertainties. The offered strategy to control has been evaluated through simulations using a MATLAB-SIMULINK model. The results showed that this strategy may effectively address instability issues created by CPL.

In this article, a method for single-phase multi-output uninterrupted power supply (UPS) has been presented with both direct current (DC) and alternating current (AC) outputs. Typically, a DC-AC UPS consists of a rectifier, a battery, and an inverter. In the proposed work, AC output is taken out from the inverter and a DC output is taken in parallel from the load side of the boost converter. In this study, the circuit is composed of an extra circuit component called a DC-DC boost converter. In a typical DC-AC UPS, usually, the input supply is from the battery, but in the presented work, a DC-DC boost converter's output is used as the supply to the inverter. Booster has been used in the model to amplify (up to 2.5 times) the output voltage of the battery without any change in the power. Booster provides more input voltage (DC) to the inverter than the battery alone could deliver. A sine pulse width modulation scheme is designed and developed to control the inverter switches. A single-phase step-up transformer has also been practised to achieve the desired output level from the inverter. In the present work, MATLAB/SIMULINK is being used for the simulation purpose of this model.

There is a drift in the automotive industry from conventional internal combustion engines (ICE) to Electric Vehicles (EV's). This drift from ICE to EV's counts to the reduced carbon emission and thus reducing the environmental pollution. EV's also finds a solution for increasing fossil fuel costs. When it comes to renewable energy sources, typically solar energy it is affluent and reliable. The usefulness of solar energy is maximized by the incorporation of advanced power converter topologies along with their advanced controls. This paper aims to compare some of the boost converter topologies that are used in EV applications with solar photo voltaic-powered charging stations. The comparative study is conducted on various parameters such as DC voltage gain, duty cycle, efficiency, voltage stress, merits, and demerits. Simulation results are analyzed and compared using the MATLAB/Simulink platform.

Power availability from renewable energy sources (RES) is unpredictable, and must be managed effectively for better utilization. The role that a hybrid energy storage system (HESS) plays is vital in this context. Renewable energy sources along with hybrid energy storage systems can provide better power management in a DC microgrid environment. In this paper, the optimal PI-controller-based hybrid energy storage system for a DC microgrid is proposed for the effective utilization of renewable power. In this model, the proposed optimal PI controller is developed using the particle swarm optimization (PSO) approach. A 72 W DC microgrid system is considered in order to validate the effectiveness of the proposed optimal PI controller. The proposed model is implemented using the MATLAB/SIMULINK platform. To show the effectiveness of the proposed model, the results are validated with a conventional PI-controller-based hybrid energy storage system.

In the present work, the undertaken power system (PS) is isolated hybrid wind–diesel PS (IHWDPS) consists of a generator based on wind turbine (GWT), a generator based on diesel engine (GDE) and an energy storing device (ESD) (for instance, capacitive energy and storage). In the present paper, an indicative analysis of frequency control also with power control for the considered IHWDPS using proportional–integral–derivative (PID) controller and cascaded PID controller aims to govern the pitch of GWT and to govern the speed governor of GDE. The controller gains have been tuned using quasi-oppositional harmony search (QOHS). The dynamic simulation reaction compression indicates that superior enactment may be recorded with cascaded PID controller over the PID controller while exposed to dissimilar perturbation. The results obtained disclose that the optimized gains of the cascaded PID controller offered with QOHS algorithm are robust in nature and do not requires resetting for extensive range for perturbations in the system.

The traffic light controlling strategy has noteworthy impressions on the traffic congestion, risks of accidents, waiting time and unnecessary consumption of fuel. But, regardless of over 50 years of researches on theory of traffic flow, the most of traffic light controlling systems are not reconfigured on a routine basis. Also, the efficiency of traffic light controlling strategy is subject to greatly on information and understanding of the circulation team. So, recently, the growing congestion in road traffic has drown portion of thoughtfulness of the researchers pool targeting to propose innovative solutions to diminish the economical losses in form of fuel cost and trip time. In this chapter, first the default traffic light controlling strategy was simulated the tripe time of each vehicle has been recorded. And, also, provide comprehensive study of the results attained with a reconfigured traffic light controlling strategy on the open source traffic simulator SUMO (Simulation of Urban Mobility) by revamping its predefined static routes during the runtime of simulation. The projected reconfigured traffic light controlling strategy has been implemented and the obtained results on the basic SUMO have established high efficiency in defining reduction in commutation or tripe time.

In the present era, electrical power system is evolving to an inverter-dominated system from a synchronous machine-based system, with the hybrid power systems (HPS) and renewable energy generators (REGs) increasing penetration. These inverters dominated HPS have no revolving body, therefore, diminishing the overall grid inertia. Such a low system inertia could create issues for HPS with REG (HPSREG) such as system instability and lack of resilience under disturbances. A control strategy, therefore, is required in order to manage this task besides benefitting from the full potential of the REGs. A virtual inertia control for an HPSREG system built with the principle of fractional order (FO) by incorporation of proportional-integral-derivative (PID) controller and fuzzy logic controller (FLC) has been projected. It is utilized by adding virtual inertia into HPSREG system control loop and referred to as FO based fuzzy PID controller for this study. Simulation outcomes states that the advocated FO based fuzzy PID controller has superior control in frequency of the system under frequent load variations. It has been noted that the proposed control scheme exhibits improved efficiency in maintaining specific reference frequency and power tracking as well as disturbance diminution than optimal classic and FO-based controller. It has been validated that, the developed controller effectively delivers preferred frequency and power provision to a low-inertia HPSREG system against high load demand perturbation. In the presented paper, analysis based on sensitivity has also been performed and it has been found that the HPSREG system’s is not effected by system parameter and load variations.

An innovative union of fuzzy controller and proportional-integral-derivative (PID) controller under the environment of fractional order (FO) calculus is described in the present study for an isolated hybrid power system (IHPS) in the context of load frequency control. The proposed controller is designated as FO-fuzzy PID (FO-F-PID) controller. The undertaken model of IHPS presented here involves different independent power-producing units, a wind energy-based generator, a diesel engine-based generator and a device for energy storage (such as a superconducting magnetic energy storage system). The selection of the system and controller gains was achieved through a unique quasi-oppositional harmony search (QOHS) algorithm. The QOHS algorithm is based on the basic harmony search (HS) algorithm, in which the combined concept of quasi-opposition initialization and HS algorithm fastens the profile of convergence for the algorithm. The competency and potency of the intended FO-F-PID controller were verified by comparing its performance with three different controllers (integer-order (IO)-fuzzy-PID (IO-F-PID) controller, FO-PID and IO-PID controller) in terms of deviation in frequency and power under distinct perturbations in load demand conditions. The obtained simulation results validate the cutting-edge functioning of the projected FO-F-PID controller over the IO-F-PID, FO-PID and IO-PID controllers under non-linear and linear functioning conditions. In addition, the intended FO-F-PID controller, considered a hybrid model, proved to be more robust against the mismatches in loading and the non-linearity in the form of rate constraint under the deviation in frequency and power front.

This work primarily focuses on electrical characteristics of a hybrid power system (HPS) incorporating renewable energy generation (REG) (HPSREG). The major components of HPSREG are the resources coordinated with multi-unit of photovoltaic cells, multi-unit of wind turbine generators, a diesel engine generator (DEG), energy storage system (ESS) with diverse nature and an electric vehicle (EV). The performance characteristics of HPSREG are determined by constant generation of power from the various sources as well as varying load perturbations. As the variation in load demand will introduce fluctuation in frequency and power with constant generation. In few of overcome the frequency and power deviation under both the above-mentioned generation and load demand conditions, proper control technique is required. In order to control the deviation in frequency and power, an integration in the environment of fractional order (FO) calculus for proportional–integral–derivative (PID) controller and fuzzy controller, termed with FO-Fuzzy PID controller tuned with quasi-opposition based harmonic search (QOHS) algorithm has been proposed. The results acquired with the proposed FO-Fuzzy-PID controller are then analyzed along with FO-PID and PID controller route for quantify effectiveness for the same under the considered cases to determine the effectiveness of the algorithm undertaken. Sensitivity investigation is also conducted in order to show the strength of the technique under study of differences in HPSREG parameters of magnitude.

Wind energy has a major contribution to the catalog of renewable energy generation and also the most efficient energy solutions driver. As the requirement and utilization for wind energy persist to develop exponentially, reduction in cost of O&M and reliability growth are the prime concerns in the maintenance strategies of wind energy conversion system (WECS). Failure of WECS results in wind turbine (WT) shutdown or expenses maintenance that extensively cut down the yearly revenue. The WECS failures result in frequent planned O&M scheduled to guarantee the reliability of wind power (WP) generation. So, condition monitoring (CM) and fault detection and diagnosis (FDD) methodology has been universally introduced for the premature fault detection in order to reduce downtime period and increase generation. If at early stages detestation and rectification are not performed, faults may lead to disastrous state with large loss of revenue. Therefore, CM and FDD of WECS’s each equipment (i.e., rotor, gearbox, drive trains, generators, and power electronics) is the need of future research. This chapter introduces and provides an analysis of the current state of CM and FDD for each key element in WECS. This work also introduces the survey with numerous possible paybacks of CM for WECS.

Voltage instability has become quite a frequent phenomenon in the existing power systems and has caused some of the major blackouts and catastrophic failures in the recent past, resulting in huge social and economic losses. The identification of subregions in power systems that experience a unique voltage instability problem is one of the most important steps of voltage stability analysis of power systems. This paper presents a method to identify voltage control area (VCA), based on coherent groups of buses, using system states obtained from synchronized phasor measurements. The coherency identification method is based on the application of principal component analysis (PCA). The coherent buses are identified by applying PCA on the angles obtained from bus voltage phasors. The results so obtained using the data from Phasor Measurement Units (PMUs) on 10-machine, 39-bus New England power system model are presented. Observing that voltage stability analysis requires assessing a high-dimensional system, the PCA technique is able to represent the system by reducing its dimension and identify groups of buses exhibiting similar features, post a disturbance.

In this work, a fractional order (FO) proportional–integral–derivative (PID) (FO-PID) controller is considered for load-frequency control (LFC) of the isolated hybrid power system, comprising of a biomass-based diesel engine generator and a wind turbine generator. The FO-PID controllers are PID controller only, and the difference lies in the order of the integral and derivative part of the controllers. In FO controllers, the order of the integral and derivative part are fractional in nature. In this paper, particle swarm optimization (PSO) algorithm has been engaged to carry out the above mentioned LFC for the considered wind-biomass isolated hybrid power system. A comparison of FO control strategy with conventional based controller techniques is made. The FO-PID controller outperforms the conventional PID controllers. And, robustness analysis is also done for the FO-PID controller.

In this paper, the fractional-order (FO) proportional-integral-derivative (PID) (FO-PID) controller is designed aiming at load-frequency control (LFC) for an isolated hybrid power system, involving a solar photovoltaic generator and a diesel engine generator. The FO-PID controller is a PID controller which has fractional order for integral and derivative. This paper engages' particle swarm optimization (PSO) algorithm to carry out the above mentioned LFC for the considered wind-biomass isolated hybrid power system. A comparison of FO control strategy with conventional based controller techniques is made. The FO-PID controller outperforms the conventional PID controllers.

Highly intermittent power, generated by wind energy in an isolated hybrid power system (IHPS), results in severe frequency and power fluctuation. The aim of this paper is to carry out a comparative study of scaling factor (SF) based fuzzy logic controller (FLC) (SF-FLC), SF-FLC with proportional-integral (PI) (SF-FLC-PI) controller, SF-FLC with proportional-derivative (PD) (SF-FLC-PD) controller and SF-FLC with proportional-integral-derivative (PID) (SF-FLC-PID) controller for deviation in frequency and power of an IHPS model. The undertaken model of IHPS for this study embraces a diesel engine generator, a wind turbine generator and an energy storage device (for instance, capacitive energy storage). Optimal tuning of the different tunable parameters considered for suppressing the deviation in frequency and power of IHPS model owing to alteration in load demand has been carried out by quasi-oppositional harmony search (QOHS) algorithm. The obtained results demonstrate minimum deviation in frequency and power may be realized by practicing the proposed SF-FLC-PID controller for the considered IHPS model. Robustness and non-linearity investigation have been also executed for the proposed SF-FLC-PID controller based configuration of the studied IHPS model. It is revealed that the proposed SF-FLC-PID controller is very much robust in nature and takes care of non-linearity very well while QOHS algorithm is adopted.

This paper describes an application of quasi-oppositional harmony search (QOHS) algorithm to design the scaling factor (SF) based fuzzy-classical controller (such as PI/PD/PID) for frequency and power control of an isolated hybrid power system (IHPS). The considered IHPS model is comprised of a wind turbine generator, a diesel engine generator and an energy storage device (such as superconducting magnetic energy storage (SMES), in this case). Traditionally, SF, membership functions and control rules are obtained in fuzzy logic controllers (FLCs) by trial and error method or are obtained based on the experiences of the designers or are optimized by some traditional optimization techniques with some extra computational cost. To overcome all these problems of FLCs, classical controllers have been integrated in this paper with the FLC. QOHS algorithm is applied to simultaneously tune the SFs (the only tunable parameter of FLC), the gains of the classical controllers and the tunable parameters of the SMES device to minimize frequency and power deviations of the studied IHPS system against various load demand and wind change. Different considered controller configurations of the IHPS are SF based FLC (termed as Fuzzy-only), SF based FLC with proportional-integral (PI) (named as Fuzzy-PI) controller, SF based FLC with proportional-derivative (PD) (abbreviated as Fuzzy-PD) controller and SF based FLC with proportional-integral-derivative (PID) (designated as Fuzzy-PID) controller. Simulation results, explicitly, show that the performance of the Fuzzy-PID controller based IHPS is superior to Fuzzy-only, Fuzzy-PI and Fuzzy-PD controller based IHPS configuration in terms of overshoot, settling time and the proposed Fuzzy-PID controller is robust against various wide range of load changes.

In this study, a novel fuzzy logic control scheme is investigated for the frequency and power control of an isolated hybrid power system (HPS) (IHPS) and HPS-based two-area power system. The studied IHPS comprises of various autonomous generation systems such as wind energy, diesel engine generator and an energy storage device (i.e. capacitor energy storage device). A novel fractional order (FO) fuzzy proportional-integral-derivative (PID) (FO-F-PID) controller scheme is deployed and the various tunable parameters of the studied model are tuned by quasi-oppositional harmony search (HS) (QOHS) algorithm for improved performance. The QOHS algorithm (based on standard HS algorithm) accelerates the convergence speed by combining the concept of quasi-opposition in the basic HS framework. This FO-F-PID controller shows better performance over the integer order-PID and F-PID controller in both linear and non-linear operating regimes. The proposed FO-F-PID controller also shows stronger robustness properties against loading mismatch and different rate constraint non-linarites than other controller structures.

This study aims to propose a more effective fuzzy logic controller (FLC) to achieve minimum deviation in frequency and power of an isolated hybrid power system. Here, in the FLC, scaling factor has been introduced and are optimized by using quasi opposition harmony search (QOHS) algorithm along with the other tunable parameters of the considered system. Performances of the scaling factor based FLC's are compared with their corresponding proportional-integral-derivative controller, in terms of several performance measures such as integral absolute error (IAE), integral of multiplied absolute error (ITAE), integral square error (ISE) and integral of time multiplied square error (ITSE), in addition to the deviation due to step load perturbations. In each case, the scaling factor based FLC model shows an extraordinarily upgraded performance over its conventional counterpart.

In this paper, the considered hybrid power system (HPS) is having a wind turbine generator, a diesel engine generator (DEG) and a storage device (such as capacitive energy storage). This paper presents a comparative study of frequency and power control for the studied isolated wind-diesel HPS with four different classical controllers for the pitch control of wind turbines and the speed governor control of DEG The classical controllers considered are integral, proportional-integral, integral-derivative and proportional-integral-derivative (PID) controller. A quasi-oppositional harmony search (QOHS) algorithm is proposed for the tuning of the controller gains. The comparative dynamic simulation response results indicate that better performance may be achieved with choosing PID controller among the considered classical controllers, when subjected to different perturbation. Stability and sensitivity analysis, presented in this paper, reveals that the optimized PID controller gains offered by the proposed QOHS algorithm are quite robust and need not be reset for wide changes in system perturbations.

In the present work, a two-area thermal-hybrid interconnected power system, consisting of a thermal unit in one area and a hybrid wind-diesel unit in other area is considered. Capacitive energy storage (CES) and CES with static synchronous series compensator (SSSC) are connected to the studied two-area model to compensate for varying load demand, intermittent output power and area frequency oscillation. A novel quasi-opposition harmony search (QOHS) algorithm is proposed and applied to tune the various tunable parameters of the studied power system model. Simulation study reveals that inclusion of CES unit in both the areas yields superb damping performance for frequency and tie-line power deviation. From the simulation results it is further revealed that inclusion of SSSC is not viable from both technical as well as economical point of view as no considerable improvement in transient performance is noted with its inclusion in the tie-line of the studied power system model. The results presented in this paper demonstrate the potential of the proposed QOHS algorithm and show its effectiveness and robustness for solving frequency and power drift problems of the studied power systems. Binary coded genetic algorithm is taken for sake of comparison.

The intermittent wind power in isolated hybrid distributed generation (IHDG) may cause serious problems associated with frequency (f) and power (P) fluctuation. Energy storage devices such as battery, super capacitor, and superconducting magnetic energy storage (SMES) may be used to reduce these fluctuations associated with f and P. This paper presents a study of IHDG power system for improving both f and P deviation profiles with the help of SMES. The studied IHDG power system is consisted of wind turbine generator and diesel engine generator. Both f and P control problems of the studied power system model are addressed in presence or absence of SMES. Fuzzy logic based proportional-integral-derivative (PID) controller with SMES is used for the purpose of minimization of f and P deviations. The different tunable parameters of the PID controller and those of the SMES are tuned by a novel quasi-oppositional harmony search algorithm. Performance study of the IHDG power system model is carried out under different perturbation conditions. The results demonstrate minimum f and P deviations may be achieved by using the proposed fuzzy logic based PID controller along with SMES.

In this paper, an autonomous isolated hybrid power system (IHPS) consisting of wind turbine generators (WTGs), diesel engine generators and an energy storage system (ESS) is considered. Due to the stochastic nature of wind, electric power generated by WTG is highly erratic and may affect power supply quality. ESSs may play an important role in controlling WTG's power output and, therefore, enabling an increased penetration of WTG in IHPS. This article deals with several ESSs like flywheel energy storage system, battery energy storage system, superconducting magnetic energy storage (SMES), capacitive energy storage (CES) and fuel cell for IHPS application. All the tunable parameters of the studied IHPS model along with those of ESS are optimized by using quasi-oppositional harmony search algorithm and comparative simulation results between various ESSs application in IHPS model for frequency and power deviation are presented in terms of rise time, settling time and steady state error for sudden changes in load/generation or both. The performance analysis of the system with different ESSs has been also carried out with different performance indices. From the simulation results it is inferred in this study that SMES and CES based IHPS perform neck to neck and these two ESSs outperform the others while controlling both frequency and power deviations.

The studied isolated hybrid distributed generation (IHDG) model of this paper consists of a wind turbine generator and a diesel engine generator. This work presents a study for improving both frequency and power deviation profiles of the studied IHDG model with the help of capacitive energy storage (CES) unit. A novel derivative-free meta-heuristic method, termed as quasi-oppositional harmony search (QOHS) algorithm, is applied to determine the optimal frequency and power deviation responses by tuning the tunable parameters of the studied IHDG model. The two fuzzy logic controllers (FLCs) are used (one at diesel unit and the other at wind generator side) to generate the output levelling frequency and power command. Each fuzzy control has two inputs, either frequency or power deviation and their respective derivatives. The two proposed FLCs are designed with QOHS algorithm for the studied CES equipped IHDG model. Performance study of the model has been carried out under different perturbation conditions. The results presented in this study demonstrate that minimum frequency and power deviations have been achieved by using the two proposed FLCs for the CES equipped studied IHDG model.

Modelling, simulation and performance analysis of a two-area thermal-hybrid distributed generation (HDG) power system having different sources of power generation has been carried out in this study. The thermal power plant is consisting of re-heat type thermal system, whereas the HDG system includes the combination of wind turbine generator and diesel generator. In the studied model, superconducting magnetic energy storage (SMES) device is considered in both the areas. Additionally, a flexible ac transmission system (FACTS) device such as static synchronous series compensator (SSSC) is also considered in the tie-line. The different tunable parameters of the proportional-integral-derivative (PID) controllers, SMES and SSSC are optimised using a novel quasi-oppositional harmony search (QOHS) algorithm. Optimisation performance of the novel QOHS algorithm is established while comparing its performance with binary coded genetic algorithm. From the simulation work it is observed that with the inclusion of SMES in both the areas, the system performances toward the achievement of minimal frequency and tie-line power oscillations are promising under different types of input loading perturbations. It is further revealed from the simulation results that the installation of an expensive FACTS device such as SSSC does not yield any significant improvement to the system performance.

In this paper, an attempt has been made to examine the effectiveness of Neuro-Fuzzy Scheme (NFS), to identify the deterioration of the winding insulation paper (WIP) in a oil-immerged power transformer, and to compare its performance over conventional methods (IEEE/IEC). The comparison of convergence characteristics of IEEE and IEC approach reveal that the NFS approach is quite faster in investigations leading to reduction in computational burden and give rise to minimal computer resource utilization. Simultaneous identification of deterioration of the WIP and operating conditions in oil-immersed power transformer has never been attempted in the past using NFS. The technique proposed in this paper provides not only best dynamic response for the deterioration of the WIP diagnosis and condition assessment of power transformer but also present its appropriate maintenance scenario as well. This approach will address a proactive assertion to the power utilities for effective realization of electrical health of oil-immersed power transformer under consideration. In this paper, testing analysis of 25 transformer samples has been carried out to demonstrates the robustness of the investigated four status conditions (Normal Operation - NO; Modest Concern - MCI; Major Concern - MCMI and Imminent Risk Failure - IRF) for wide changes in operating condition and loading condition perturbation. © 2013 Elsevier Ltd. All rights reserved.

The measurement of the frequency response of power transformers is a diagnostic methodology for detecting winding deformation and core displacement (along with other mechanical and electrical diagnostic methodology), which acts as the most important agents for the detection of mechanical failure in transformers. There are two different methods to carry out the measurement of frequency response: Sweep Frequency Response Analysis - (SFRA) and Low Voltage Impulse - LVI. SFRA has the upper hand over LVI such as: higher signal to noise ratio, higher repeatability and reproducibility and less measuring equipment required. It is based on comparison between; 1) earlier measurement on same transformer, 2) measurement on sister transformers, or 3) phase to phase comparison on same transformer with higher signal to noise ratio, higher repeatability and reproducibility and less requirement regarding measuring equipment. SFRA is an electrical test that provides information relating to transformers mechanical integrity. This paper details with use of sweep frequency response analysis (SFRA) as a diagnostic methodology to detect winding deformation and core displacement in power transformers. Practical case studies are presented that demonstrates the effectiveness of this methodology.

This communication presents a study showing that mobile phone usage duration have got a significant effect on the EMI characteristics. This is because the long-time operation (transmitting and receiving) of the mobile phone cause their hardware to worn-out over a period of time to significant extent. Hence, the operation of worn-out elements introduces errors that degrade the performance of the power flow control algorithms along with the hardware, which in turn result in poor power flow control in different hardware of mobile phone. This leads to the generation of interference and compels a handset to violate the EMC regulations. This degrades the quality of the mobile phone services and leads to malfunctioning of electric/electronic devices in its vicinity and also to the systems connected to the same grid. © 2012 IEEE.

The gases generated in oil filled transformer can be used for determination of incipient faults. Dissolved gas analysis (DGA) of transformer oil has been one of the most powerful methods to detect the faults. The various methods such as liquid chromatography, acoustic analysis, and transformer function techniques require some experience to interpret observations. The researchers have used artificial intelligence (AI) approach to encode these diagnostic techniques. This paper presents an expert system using AI techniques which can diagnose multiple faults in a transformer theoretically and practically using fuzzy-logic information model. We also concluded by identifying limitations, recent advances and promising future research directions over seventy and more power transformers.