This work presents the power factor correction (PFC) buck-boost converter for on-board electric vehicle (EV) charging applications. The PFC buck-boost converter is designed to operate in discontinuous current conduction mode (DCCM), thus achieving natural PFC for the universal input voltage range. In addition, DCCM operation does not require input voltage or current sensors; as a result, the control is more reliable and economical than continuous current conduction mode (CCCM). Furthermore, the buck-boost converter switch operates in zero current switching (ZCS) which results in reduced switching losses and improves the efficiency. The detailed steady-state analysis, operating modes, and design analysis for DCCM operation are presented. To validate the theoretical studies, a closed-loop voltage mode control of the PFC buck-boost converter is developed and tested in a PSIM software environment. The simulation results uphold the converter analysis and achieve a high power factor and low total harmonic distortion (THD) for the universal input range

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

This paper proposes a Random Forest (RF) machine learning algorithm-based prediction model for the state of charge (SoC) level of lithium-ion batteries for electric vehicles. To show the effectiveness of the proposed prediction model performance, the RF model has been compared with the other machine learning algorithms such as Support Vector Machines (SVM) and Gradient Boosting (GB) approaches. The dataset includes cell temperature, state of charge (SoC), voltage, and current readings at three different external temperatures—15, 25, and 30 degrees Celsius are considered in this paper to test the performances of the proposed model. After preprocessing of the dataset, 20% of the data was used for testing and the remaining 80% for training purposes. The various metrics such as mean squared error (MSE), mean absolute error (MAE), coefficient of determination (R2), root mean squared error (RMSE), normalized root mean squared error (NRMSE), residual standard error (RSE), and relative absolute error (RAE) are usually preferred to evaluate the performance of the prediction models. The simulation results of the proposed model clearly show the effectiveness of SoClevel estimation for real-time battery management systems (BMS) when compared to other machine learning algorithms. The efficiency of the proposed model is 99% and execution time is less than 5 seconds. The accurate estimation of the SOC of lithium-ion batteries is crucial for optimizing battery performance, ensuring safety, and extending battery life in electric vehicles

Accurate estimation of battery state including state of charge (SoC) and state of health (SoH) are crucial for ensuring safety in energy storage applications. The SOC and SOH estimators were independently trained using the same input vector but with different objective functions, no integration between SOC and SOH estimations were explored. In this paper, a unified algorithm, for identifying both SoC and SoH states, is introduced by considering the Bayesian optimization for hyperparameter tuning. This approach allows seamless transition between SoC and SoH estimation without needing separate models for each task. In addition, equipping the dual estimation framework with a unified algorithm for identifying both states would impact the algorithm’s complexity. The suggested BiLSTM model reduces complexity in real-time Battery Management System (BMS) applications by eliminating the need for a separate model to estimate SoH. When compared to other machine learning and deep learning models such as Support Vector Machines (SVM), Decision Trees (DT), Random Forest (RF), Radial Basis Function Neural Networks (RBF-NN), Recurrent Neural Networks (RNN), and LSTM, the suggested BiLSTM method demonstrates the highest efficiency. Finally, to verify the proposed method’s effectiveness, a comparison among the different evaluation metrics was conducted. The proposed BiLSTM model achieved an average MAE (Mean Absolute Error) of 0.08 and NRMSE (Normalized Root Mean Squared Error) of 0.15 for SoC estimation across various temperatures (5?C,15?C, 35?C, and 45?C), and an MAE of 3.12 and NRMSE of 0.23 for SoH estimation with a degradation rate of 47% of the cell estimated from the predicted capacity values

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

This paper presents a bidirectional AC-DC converter system designed for seamless power exchange between electric vehicles (EVs) and the utility grid. The proposed converter facilitates the conversion of 230 V, 50 Hz AC input to 380 V DC during grid-to-vehicle operation, allowing for efficient battery charging through a bidirectional DC-DC converter. Conversely, during vehicle-to-grid operation, it converts the 380 V DC input from the DC-DC converter to 230 V, 50 Hz AC output for grid supply. The system employs PI controllers to ensure precise voltage and current regulation, ensuring stable and efficient operation during grid interaction. Simulation results demonstrate the system’s effectiveness in managing power conversion for both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) applications This paper presents a bidirectional AC-DC converter system designed for seamless power exchange between electric vehicles (EVs) and the utility grid. The proposed converter facilitates the conversion of 230 V, 50 Hz AC input to 380 V DC during grid-to-vehicle operation, allowing for efficient battery charging through a bidirectional DC-DC converter. Conversely, during vehicle-to-grid operation, it converts the 380 V DC input from the DC-DC converter to 230 V, 50 Hz AC output for grid supply. The system employs PI controllers to ensure precise voltage and current regulation, ensuring stable and efficient operation during grid interaction. Simulation results demonstrate the system’s effectiveness in managing power conversion for both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) applications

A customized multi-level inverter configuration designed for driving an induction motor with multiple pole pairs is introduced. Within the induction motor, each pole pair winding coil spaced 360° (electrically) apart maintains the same voltage profile. In our case, two windings in a four-pole induction motor are deliberately disconnected. A dual two-level inverter is used to power each half of the winding, so two such inverters are used to feed the entire stator winding of the induction motor as pole pair windings are disconnected. The single DC source used to power these inverters has a magnitude of Vdc/4, or 25% of input voltage DC bus voltage needed to power a typical Neutral Point Clamped five-level inverter. This new Pulse Width Modulation approach is used to cancel the harmonics at first center band while controlling the inverter output voltage. This method successfully lowers torque ripple by reducing current ripple. Furthermore, power balancing problems are eliminated because the single DC source is supplying the entire topology. The capacitor voltage balancing problems are also resolved because this design is derived using only two-level inverters. Very few changes to the design are needed for the suggested topology; the main change is to disconnect winding coils with the same voltage profile. The efficacy of the proposed inverter employing the innovative PWM technique in the linear modulation region is demonstrated by simulation results utilizing a 5-hp four-pole induction motor in MATLAB (Simulink)

Lighting systems using light emitting diode (LED) have drawn significant attention across the world. Nevertheless, to maintain a steady light output, these systems necessitate constant current regulators. This article proposes a buck–boost integrated zero voltage switched full?bridge LED converter with low current stress. It powers two identical lamps and a lamp of power rating twice the identical lamp. A direct current voltage source, arranged in series, transmits a portion of light power without conversion. A regulated low power output is provided using a complete bridge converter. The semiconductor switches off the full?bridge converter carry minimal current. This characteristic lowers conduction losses. The suggested converter facilitates dimming operation via on–off control and zero voltage switching, leading to minimal switching losses. Further input voltage of full?bridge converter is modulated to maintain constant LED lamp current. The detailed steady?state analysis and implementation of the proposed full?bridge LED converter with dimming control operation is presented here.

This work investigates the application of Artificial Bee Colony (ABC) optimization for the design of Type compensators utilizing the dual-loop control scheme. The proposed Type compensators integrate the ABC optimization for regulating the closed-loop operation of a DC-DC buck converter. Such an integration of ABC optimization, aids in effectively regulating the output voltage and inductor current, besides ensuring enhanced time domain criteria. The proposed dual-loop control scheme consists of a current loop and a voltage loop. The current loop regulates the inductor current and the voltage loop regulates the output voltage. The efficacy of the proposed method is revealed through extensive simulation and experimental investigation under start-up response, step perturbations in external load. The experimentation is conducted on a laboratory prototype using dspace DS1104 control board.

A novel zero-voltage switching full-bridge (NZVSFB) converter designed for multiple load LED driver applications is introduced in this paper. Four LED lamps are involved in this configuration, with Lamp-2, Lamp-3, and Lamp-4 being powered by a full bridge converter, and Lamp-l being directly connected in series with the battery source. The efficiency of the system is increased since the power provided to lamp-l comes directly from a battery source, eliminating the need for any power processing stage. The major claims of the proposed NZVSFB converter are low component count/lamp, enhanced efficiency, zero voltage switching (ZVS) of all the switching devices, ripple free current and equal current sharing. The interleaved technique utilized in inductor design aims to mitigate the adverse effects of ripple currents on LED performance and circuit reliability by reducing their magnitude and ensuring more stable operation. The steady state operation of the proposed NZVSFB converter is discussed in detail and the effectiveness of the circuit is verified in MATLAB Simulink environment.

This article presents an intelligent adaptive neural control scheme to track the output speed trajectory in power converter?driven DC motor system. The proposed technique integrates an adaptive polynomial?neural network with a backstepping strategy to yield a robust control system for output tracking in DC motor. Such a unification of online neural network?based estimation and adaptive control, results in effective regulation of the output across a wide load torque uncertainties, besides yielding a promising transient and steady?state performance. The stability of the entire closed?loop system is ensured through Lyapunov stability criterion. The efficacy of the proposed strategy is revealed through an extensive experimental investigation under various operating points during start?up, step?reference tracking, and external step?load torque disturbances. The real?time experimentation is conducted on a laboratory prototype of power converter?driven DC motor of 200W, using dspace DS1104 control board with MPC8240 processor. The results obtained confirm an improvement in the transient response of the output speed by significantly reducing the settling time to and yielding a steady state behavior with no peak over/undershoots during load disturbances, in contrast to other similar works presented in the literature intended for same the application.

In contemporary energy production, there's been a significant transition from coal-centric methods to renewable energy sources (RES) that emit zero pollutants. As RES becomes more integral to expansive power systems, there's a growing need for regulated power electronic systems. When integrated with microgrids, RES often face stability challenges, being represented in DC microgrids as a constant power load (CPL). The DC-DC converters designed to operate these CPL loads are affected by switching irregularities and the destabilizing effects of CPL, leading to broader power system instability. This study introduces a backstepping control (BSC) approach for a DC-DC buck converter operating with CPL. Through extensive experimental investigations, the effectiveness of the proposed controller under various test conditions, contrasting its results with the cascade PI controller have been evaluated. The outcomes reveal that the proposed backstepping control technique enhances both the dynamic and steady-state performance of the DC-DC buck converter-CPL system, especially during extensive fluctuations in the load power.

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.

LED lighting has emerged as a global solution for sustainable industrial lighting. The three-leg asymmetrical voltage resonant converter has been designed for optimal performance in dimmable Light-emitting diode (LED) lighting applications. Its distinctive features, including enhanced efficiency, asymmetrical voltage regulation, Zero Voltage Switching (ZVS) of all power switches, and an independent dimming control strategy, make it well-suited for high-power LED lighting applications. The converter under consideration incorporates a common leg-1 to drive load-1 and load-2. So that, the load-1 and load-2 are powered by the asymmetrical voltage between leg-1 and leg-2, and leg-1 and leg-3 respectively. To enable independent dimming control, the voltages between legs are nullified by individual dimming levels. The proposed circuit incorporates two resonant circuits. This configuration ensures that all power switches operate with ZVS, effectively minimizing switching losses. Further, the threshold voltage of each LED load is supplied by the battery in turn resulting in a lower power processing of the converter. The detailed operational principle and design considerations are discussed. Moreover, the loss analysis, detailed comparison with similar works, and the efficiency analysis at different dimming conditions are presented. Finally, the effectiveness of the converter is verified in a MATLAB Simulink environment and an 80W laboratory prototype.

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.

The rapid increase in demand for electricity and the emergence of the smart grid have dealt with optimistic opportunities for home energy management systems. The smart home with the integration of renewable energy sources such as photovoltaic systems, micro-wind turbines, and battery storage can provide in-house power generation and also give the option of exporting power to the grid. This paper mainly proposes a centralized coordinated neighborhood power-sharing with incentive-based energy management for multiple smart home consumers. The incentive method and various pricing schemes like time of use and feed-in tariff are considered in this paper to determine the electricity billing of all smart home consumers. Due to these incentives and pricing schemes in this model, all smart home consumers are encouraged to be involved in neighborhood energy sharing. A group of ten smart homes with various load profiles and RER energy integration is considered as a test system to determine the performance of the proposed neighborhood smart home energy management model. The simulation results show that the centralized neighborhood-coordinated smart home energy management model can provide significant economic benefits to all smart home consumers when compared to the without neighborhood power-sharing case.

This article presents a promising self-learning-based robust control for output voltage tracking in DC–DC buck power converters, particularly for applications demanding high precision performance in face of large load uncertainties. The design involves a computationally simple online single hidden layer neural network, to rapidly estimate the unanticipated load changes and exogenous disturbances over a wide range. The controller is designed within a backstepping framework and utilizes the learnt uncertainty from the neural network for subsequent compensation, to eventually ensure an asymptotic stability of the tracking error dynamics. The results obtained feature a significant improvement of dynamic and steady-state performance concurrently for both output voltage and inductor current in contrast to other competent control strategies lately proposed in the literature for similar applications. Extensive numerical simulations and experimentation on a developed laboratory prototype are carried out to justify the practical applicability and feasibility of the proposed controller. Experimental results substantiate the claims of fast dynamic performance in terms of 94% reduction in the settling time, besides an accurate steady-state tracking for both output voltage and inductor current. Moreover, the close resemblance between computational and experimental results is noteworthy and unveils the immense potential of the proposed control system for technology transfer.

This article proposes a novel Zernike radial neural network based adaptive control architecture for closed-loop control of output DC voltage in DC–DC buck power converter. The proposed combination of novel Zernike radial neural network estimator and the adaptive backstepping controller effectively compensates for wide range of perturbations affecting the converter system, in an online manner. The closed loop stability of the DC–DC buck power converter with the proposed neuro-adaptive backstepping controller is shown using Lyapunov stability criterion. Numerical simulations are conducted to examine the effectiveness of the proposed controller under start-up response and step changes in the load, source voltage and reference output voltage. Furthermore, the simulation findings are validated by conducting extensive real-time investigation on a laboratory prototype, under a wide range of operating points. The results obtained show a significant improvement in the transient response of both output voltage and inductor current of the converter, relative to the relevant control methods proposed in the recent past.

An efficient soft-switched light emitting diode (LED) driver with input regulation is proposed. The converter drives multiple lamps, and it is divided into two sections. Lamp-2 and lamp-3 are driven by a full bridge converter (FBC), while lamp-1 is placed in series with the input dc voltage source. Power is delivered to lamp-1 without passing through the FBC, which results in improved efficiency. The main benefits of the presented LED driver are: 1) lower current ratings of the FBC switches; 2) ripple-free lamp currents; 3) zero voltage switching (ZVS); 4) high power efficiency; 5) drives multiple lamps; 6) input regulation for source variation; and 7) lower components per lamp. To reduce the current rating of FBC switches, two identical lamps are powered using interleaved inductors. Owing to this, the lamps experience ripple-free currents. Further, due to this, the ZVS is achieved which results in high efficiency. A closed-loop buck-boost converter will compensate for the variations in input by adjusting the duty cycle. The converter operating modes, steady state, and efficiency analysis are discussed in detail. Moreover, to indicate the performance of the converter, a 130 W prototype is built, and experimental results are presented.

A new three-leg asymmetrical voltage resonant converter is proposed for multiple LED load applications. The converter is developed with leg-1 has common for both LED loads. The main contributions of the proposed work are: (1) Independent dimming control of LED loads, (2) Zero Voltage Switching (ZVS) of all the power switches, (3) High efficiency and (4) Regulation with asymmetrical voltage control. To achieve independent dimming control, the PWM dimming is employed with leg-2 and leg-3. Two resonant circuits are connected in the proposed circuit. Owing to this all the power switches operate with zero voltage switching which reduces the switching losses, resulting in higher converter efficiency. The loads can be regulated using asymmetrical voltage control. The threshold voltage for the LED loads is supplied with battery source, therefore power processing of converter is reduced. The operating modes and steady-state analysis of the proposed converter are presented in detail and verified by using the MATLAB Simulink environment.

Direct torque control (DTC) is one of the most prominent control techniques used by permanent magnet synchronous motor (PMSM) drives in industry applications. Nevertheless, the presence of hysteresis controllers and inaccurate voltage switching table in traditional DTC results in higher torque and flux ripple. This study proposes an enhanced DTC functioned Surface-mounted PMSM (S-PMSM) drive with mitigation of torque and flux ripple. The operation relies on generating the reference voltage vector (VV) in a stationary reference frame, which supports control of torque and flux without hysteresis controllers. The reference VV generation is simple and does not affect control robustness. The position of reference VV in a sector is used to build the voltage vector (VV) switching table. As a result, the application of nearest discrete VV to reference VV produces optimal torque and flux control. Moreover, redundant switching combinations of null VV are effectively used for possible minimization of switching frequency of two-level voltage source inverter (VSI) supplied S-PMSM drive. Therefore, proposed DTC gains improved S-PMSM drive response along with switching frequency reduction. In dSPACE-RTI 1104 platform, experimental response of S-PMSM drive under various operating conditions have been depicted to highlight the proficiency of proposed DTC in comparison with existing DTC.

Applications of power electronic converters have increased invariably in fields of engineering such as robotics, e-mobility and smart grids. DC-DC converters are employed as a switching devices to obtain a required amount of DC voltage in various industrial applications. Under the class of non-isolated DC-DC power converters, the buck converters are of specific interest, as they provide lower DC output voltage than the source DC voltage. In order to obtain a faithful output voltage tracking despite disturbances affecting the system, the converter is connected in the closed feedback loop. In this respect, this paper presents the design, development and experimental findings of Laguerre neural network driven adaptive control of DC-DC buck power converter. The stability of the proposed controller is established through Lyapunov stability criterion. Further, the results are compared with adaptive backstepping control method, by subjecting the converter to start-up test, step changes in the load resistance, input voltage and reference voltage tests. Thereafter, the performance is evaluated on DSP-based dSPACE 1104 processor in the laboratory. Finally, the results are compared in terms of settling time of output voltage state. The results indicate an enhanced dynamic performance of both output voltage and inductor current with the action of proposed controller, thus making it suitable for fast practical applications.

Nowadays, Predictive Torque Control (PTC) strategy is recognized as a strong tool for controlling the motor drive. Intuitive and multi-objective controlling are significant benefits of PTC. Owing to these benefits, its application is introduced for Open Winding Permanent Magnet Synchronous Motor (OW-PMSM) drive. The basic PTC operated OW-PMSM drive consequences high Common Mode Voltage (CMV) and leads to early failure of motor bearings. In this paper, CMV elimination is proposed for OW-PMSM drive using voltage vector selection in PTC operation. In proposed PTC, the possible voltage vectors (VVs) are identified to gain zero CMV and preselected as prediction VVs for cost-function evaluation. From cost-function evaluation, optimal VV is considered for controlling OW-PMSM drive. In addition, decrease in switching frequency is achieved through proper utilization of optimal VV’s redundant switching states. Therefore, the overall modifications in proposed PTC of OW-PMSM drive ensure simple operational control, CMV elimination, and switching frequency and loss reduction. The claims of proposed PTC are verified through Matlab/Simulink platform and its proficiency is highlighted against basic PTC operation. Thus, the enhanced operation of proposed PTC for OW-PMSM drive with zero CMV is justified.

The Light Emitting Diodes (LEDs) are gaining more importance in several lighting applications due to their advantages, such as high efficiency, long life, and environment friendliness, over conventional lighting sources. The driver circuit is a significant component in an LED lighting system to provide regulated power to the lamp. Numerous, DC-DC converter topologies have been proposed for LED lighting applications. Under which low- and medium-power lighting applications such as domestic lighting, traffic lighting, and decorative lighting, non-isolated driver circuits are more beneficial. However, in high-power applications such as street lighting and industrial lighting, isolated and soft switching converters are mostly used as LED driver circuits. Due to high-power capability, reduced switching losses, less component count, high frequency of operation, and high efficiency, soft switching converters are drawing more attention in high-power applications. This paper presents a comparative analysis of resonant LED driver topologies proposed for multiple load lighting applications. Simulations of a few full bridge LED driver topologies have been carried out using MATLAB/Simulink environment. Various performance parameters are evaluated, and finally, conclusions are drawn.

Lighting systems using light-emitting diode (LED) have drawn significant attention across the world. This is due to their promising features such as high energy efficiency, reduced greenhouse gas emission, and eco-friendly nature. However, these systems require constant current regulators to provide constant illumination. This article proposes a soft-switched full-bridge LED driver circuit for dc-grid applications with dimming control operation. The circuit consists of a soft-switched full-bridge converter to power different LED lamps with reduced device count from dc-grid voltage. The semiconductor switches of the full-bridge converter conduct a small current during on time due to interleaved inductor and equal current sharing of lamp-2 and lamp-3. This feature reduces the conduction losses. In addition, the proposed converter yields less component count per lamp, dimming operation through on–off control and zero voltage switching, which results in low switching losses. The detailed steady-state analysis of the proposed converter for dc-grid applications with dimming control operation is presented in this work. The performance of the proposed converter is compared with other similar topologies available in the recent literature. Numerical simulations and real-time experimental validations are conducted to evaluate the steady-state performance of the proposed converter topology for LED applications, driving multiple lamp loads from dc-grid. It has been established that the efficiency of the proposed full-bridge converter is 97.52% at the rated power.

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.

This study initiates the implementation of fractional-order (FO) fuzzy (F) PID (FOFPID) controller fine-tuned using a seagull optimization algorithm (SOA) for the study of load frequency control (LFC). Initially, the SOA-tuned FOFPID regulator is implemented on the widely utilized model of dual-area reheat-thermal system (DARTS), named test system-1 in this work for a perturbation of 10% step load (10% SLP) on area-1. Dynamical analysis of the DARTS system reveals the viability of the SOA-tuned FOFPID control scheme in regulating frequency deviations effectively compared to other control schemes covered in the literature. Later, the presented regulator is implemented on the multi-area diverse sources (MADS) system possessing realistic constraints in this study, termed test system-2. The sovereignty of the presented FOFPID controller is once again evidenced with controllers of PID/FOPID/FPID fine-tuned with the SOA approach. Moreover, the effect of considering practical realistic nonlinearity constraints such as communication time delays (CTDs) on MADS system performance is visualized and the necessity of its consideration is demonstrated. Furthermore, AC-DC lines are incorporated with the MADS system to enhance the performance under heavy-load disturbances and the robustness of the proposed regulatory mechanism is deliberated.

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.

Angular velocity control in DC-DC converter-driven direct current (DC) motors exhibit several challenges in numerous applications. This article proposes a novel single functional layer Legendre neural network integrated adaptive backstepping control technique for the DC-DC step down converter-permanent magnet DC (PMDC) motor system. The proposed controller first aims to estimate the uncertainties in an online mode and then compensate the same efficiently during the robust control action. The closed loop feedback stability of the entire system under the action of proposed controller and the online adaptive learning laws are proved using Lyapunov stability criterion. Further, the proposed controller is numerically simulated for various test conditions including; (a) startup response, (b) a step change in the load torque and (c) reference angular velocity tracking. The transient performance measures of angular velocity such as peak overshoot, peak undershoot and settling time have been observed under the proposed control design and compared with the response obtained from proportional-integral-derivative (PID) controller. Finally, the results presented demonstrate the efficacy of the proposed controller in yielding an enhanced performance under both nominal and perturbed test conditions over a wide operating range.

A high gain boost converter fed single-phase voltage source inverter with its control for DC to AC power conversion in uninterrupted power supply and renewable energy applications is presented in this paper. The conventional DC-DC boost converter with a coupled inductor and switched capacitor is utilized to obtain high gain. Further, the output voltage of the inverter is controlled by sinusoidal pulse width modulation technique. The detailed design and analysis of high gain boost converter fed single-phase voltage source inverter is also presented. The sine pulse width modulation control scheme for the voltage source inverter is developed and presented. In order to validate the high gain boost converter fed single-phase voltage source inverter, the simulation model is developed in LTspice software environment and results are validated. The results show high gain boost converter achieves a gain of about 10 and the single-phase voltage source inverter is able to provide a rms voltage of 228 V without using the step-up transformer. The total harmonic distortion of output current is found to be reduced below 4%. Further, the results obtained are found to be in close agreement with theoretical values.

This paper presents the integration of renewable energy sources such as photovoltaics, wind, and batteries to the grid. The hybrid shunt active power filter (HSHAPF) is optimized with the Gray wolf optimization (GWO) and fractional order proportional integral controller (FOPI) for harmonic reduction under nonlinear and unbalanced load conditions. With the use of GWO, the parameters of FOPI are tuned, which effectively minimizes the harmonics. The proposed model has effectively compensated the total harmonic distortions when compared with without the filter and with the passive filter, the active power filter with a PI controller, and the GWO-FOPI-based controller. The performance of the proposed controller is tested under nonlinear and unbalanced conditions. The parameters of the FOPI controller are better tuned with the GWO technique. The comparative results reflect the best results of GWO-FOPI-based HSHAPF. The suggested controller is built in 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.

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