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

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

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 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

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

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

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 100 W are presented

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 userfriendly simplicity. The proposed study uses the Fuzzy Adaptive Differential Evolution (FADE) analysis for microgrid planning integrated with EV charging infrastructure, using the IEEE 33bus 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

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 LevelShifted 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 inverterfed 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

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.

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,annualutilitybillsavingsof281,905, and a payback period of 5.2 years

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.