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The maximum power point tracking (MPPT) mechanism is one of the important components of a solar photovoltaic system (SPS). For efficient working of the SPS, the MPPTs must be flexible to adapt to any change in irradiance level, as the output from the Solar PV system majorly depends on the level of irradiance present at a geographical location. This study deals with the scope of improvement in artificial neural network (ANN) by incorporating the metaheuristic tuning. The modified version of ANN MPPT controller has been developed with the help of a novel Horse herd optimization (HHO) algorithm that strategically optimizes the weights of the neural network. The systematic implementation and comparison of different versions of ANN MPPT controller have been performed, taking into account different scenarios like dynamically changing irradiance levels and several cases of partial shading condition. For the training of the neural network, the irradiance and temperature dataset has been collected from the NASA website. The analysis of the results has been done using parameters like fill factor, maximum power tracked, percentage mismatch power loss and efficiency, which shows that the ANN MPPT controller tuned with HHO algorithm tracks maximum power and attains maximum efficiency under various considered scenarios. Further, the EN50530 MPPT efficiency test has been performed under the fast- and slow-changing irradiance level to compare the efficacy of different algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid NBO–SDRN approach for a solar PV (SPV) array fed water pumping system utilizing a single-ended primary inductor converter (SEPIC) based BLDC motor drive. The proposed hybrid method combines Namib beetle optimization algorithm (NBO) and spiking deep residual networks (SDRN). Commonly, it is named the NBO–SDRN technique. The main goal of the NBO–SDRN method is enhanced energy efficiency, improved control algorithms, and optimal utilization of available solar energy resources. This paper focuses on integrating a SEPIC converter-based brushless DC motor drive in such systems to optimize energy conversion, enhance control, and promote environmentally friendly water supply. The BLDC (brushless direct current) motor drive and advanced control algorithms enable precise speed and torque control, improving overall energy efficiency and adaptability to varying water demand. By then, the NBO–SDRN method is done in the MATLAB working stage, and the execution is calculated with the present procedures. The proposed approach performs better outcomes in all existing singular spectrum analysis (SSA), heap-based optimization (HBO), and bee circle search algorithm (CSA) methods. From the result, the NBO–SDRN method-based efficiency is 95% higher than the existing methods. [ABSTRACT FROM AUTHOR]

An improved combined-mode power converter (CMPC) for a solar photovoltaic (SPV) system is presented in this paper. Ten-power electronic switches are utilised for the functioning of the converter. The proposed converter has four modes of operation: buck, boost, inverter, and mixed mode. The new CMPC topology provides a unique and complex switching sequence for implementing the mixed-mode operation. This work uses a field-programmable gate array (FPGA) for mode selection and to create PWM (MS-PWM) signals. A digital MS-PWM controller is configured to switch between improved stepped perturb and observe (ISPO) maximum-power point tracking (MPPT) and sine wave PWM (SPWM). The ISPO provides the appropriate duty cycle for buck/boost operation, while the SPWM controls the inverter operation. This work's new digital PWM control algorithm supports mode selection, PWM generation, and reconfiguring PWM generation timings. The whole framework for constructing the MS-PWM controller employs a very high-speed integrated circuit hardware description language (VHDL). Simulation and experimental results are presented, proving that the digital MS-PWM controller uses fewer resources with improved accuracy. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV)-based power generation systems are becoming increasingly popular as a due to its high performance and cleanliness. Several factors influence the performance of a PV system, including shadowing effects. PV systems employ MPPT methodologies to obtain the power from PV array. Conventional MPPTs works well under normal conditions when there is no shadow effects or partial shading. The presence of partial shading affects the system performance and generates several power peaks. This complicates the process of finding out of the global peak (GMPP) with improved tracking efficiency and reduced settling time including conversion efficiency. This work proposes three hybrid MPPT techniques: Water Cycle Optimisation-Perturb and Observe (WCO-PO), Artificial Neural Network Supported Adaptable Stepped-Scaled Perturb and Observe (ANN-ASSPO), Grey Wolf Optimisation-Modified Fast Terminal Sliding Mode Controller (GWO-MFTSMC), and two conventional MPPT techniques WCO and P&O have been implemented. The proposed system utilizes interleaved boost converter with three phase. The performances of proposed hybrid MPPTs strategies were compared in terms of output voltage, output current and extracted power. The comparison also includes conversion efficiency and average settling time. To analyse the performances, four different cases have been used to test the efficacy of hybrid MPPTs under changing climatic conditions. The MATLAB/Simulink tool has been used to analyze the PV system performances. In the three hybrid MPPT techniques, WCO-PO has performed better when compared to other two hybrid MPPTs in terms of conversion efficiency (99.56%) and settling time (1.4 m). [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms are particularly useful for maximum power point tracking (MPPT) applications, because they can adapt to changes in operating conditions and effectively handle partial shading conditions. However, metaheuristic algorithms also have some limitations that need to be addressed to make them suitable for MPPT applications. The problems associated with metaheuristic algorithm-based MPPT applications include being trapped in local optima, slow convergence speed, shading condition variability, computational complexity and robustness. These problems lead to reduced efficiency in MPPT applications. In the literature, the solution of the aforementioned problems is partially addressed and some of them are solved via an additional irradiation sensor. The motivation of this study is to develop a control algorithm that covers all problems that have been partially solved in the literature and includes an original re-initialization modeling method in accordance with visual programing concept, without using any additional radiation sensor. The proposed control algorithm has the flexibility to be easily adapted to other metaheuristic algorithms and does not require any radiation sensors. The re-initialization model created via Matlab/Simulink and "Embedded Coder Support Package for TI C2000 Processors" allows easy tracking of the global maximum power point (GMPP) by detecting variable radiation conditions. The proposed model was implemented on the Cuckoo Search Algorithm (CSA) and verified through experimental studies carried out with a TI-TMS320f28069 microcontroller and PV emulator. The experimental results confirm that issue 1 is solved with 100%, issue 2 is solved with 99.5%, issue 3 is solved with 99.84%, and issue 4 is solved with 100% MPPT efficiency. [ABSTRACT FROM AUTHOR]

This work presents an improvement of the incremental conductance technique by adding PI controllers optimized by the recent equilibrium optimization algorithm. It addresses the main weaknesses of the incremental conductance algorithm consisting of the choice of step-size and the elimination of oscillations around the MPP. The proposed approach makes the MPPT control more dynamic and self-adaptive against the atmospheric conditions change. It has been applied for a photovoltaic generator, the thermoelectric generator and for the hybridization of the two generators. The simulation results, and the comparative study with the optimized step-size P&O and Incremental conductance methods, prove the efficiency and superiority of the proposed technique in terms of energy extraction and speed. The introduced work is developed in the MATLAB/Simulink environment. [ABSTRACT FROM AUTHOR]

The energy generation efficiency of photovoltaic (PV) systems is compromised by partial shading conditions (PSCs) of solar irradiance with many maximum power points (MPPs) while tracking output power. Addressing this challenge in the PV system, this article proposes an adapted hybrid control algorithm that tracks the global maximum power point (GMPP) by preventing it from settling at different local maximum power points (LMPPs). The proposed scheme involves the deployment of a 3 × 3 multi-string PV array with a single modified boost converter model and an adapted perturb and observe-based model predictive control (APO-MPC) algorithm. In contrast to traditional strategies, this technique effectively extracts and stabilizes the output power by predicting upcoming future states through the computation of reference current. The boost converter regulates voltage and current levels of the whole PV array, while the proposed algorithm dynamically adjusts the converter's operation to track the GMPP by minimizing the cost function of MPC. Additionally, it reduces hardware costs by eliminating the need for an output current sensor, all while ensuring effective tracking across a variety of climatic profiles. The research illustrates the efficient validation of the proposed method with accurate and stable convergence towards the GMPP with minimal sensors, consequently reducing overall hardware expenses. Simulation and hardware-based outcomes reveal that this approach outperforms classical techniques in terms of both cost-effectiveness and power extraction efficiency, even under PSCs of constant, rapidly changing, and linearly changing irradiances. [ABSTRACT FROM AUTHOR]

Solar energy is the most promising among many renewable energy sources to meet the increasing demand. Photovoltaic (PV) based power generating solutions are expected to gain popularity as a power source for different applications, including independent and grid connected loads, due to their cleanliness, high performance, and high dependability. The efficacy of photovoltaic systems is impacted by several elements, including geographical location, positioning, shadowing effects, and local climate conditions. In order to fulfil the demands of loads, an interleaved boost converter is utilized, which has a reduced number of filters with less stress on the devices. Solar powered systems employ several maximum power point tracking (MPPT) methodologies. However, when there is partial shading, many power peaks arise, which complicates the identification of the overall peak. Although MPPT approaches are designed to measure and maintain the global maximum power point (GMPP), there are still significant oscillations observed around the GMPP with subpar settling time, tracking efficiency, and conversion efficiency. In this work, novel hybrid MPPT technique called artificial neural network supported adaptable stepped-scaled perturb and observe (ANN-ASSPO) method and water cycle optimization based perturb and observe (WCO-PO) have been proposed. Artificial neural network (ANN) has been used to determine the best scaling factor in ANN-ASSPO MPPT. Performance is enhanced in ANN-ASSPO MPPT by using the optimum scaling factor, particularly in situations when the irradiance is rapidly changing/partial shading conditions. Similarly, in WCO-PO MPPT water cycle optimization is used to determine the peak power when the PV panel is subjected to partial shading conditions. The performances of proposed hybrid MPPT ANN-ASSPO and WCO-PO techniques have been compared in terms of power generated, output voltage, average settling time and conversion efficiency. The MATLAB/Simulink tool is employed to carry out the experiment for this study. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-objective H2/H ∞ maximum power tracking control of a variable speed wind turbine to minimize the H2 tracking error and ensure the H ∞ model reference-tracking performance, simultaneously. The optimal condition is obtained via a boost converter use, which adapts the load impedance to the wind turbine generator. Thus, based on the fuzzy T-S model, a multi-objective Maximum Power Point Tracking (MPPT) controller is developed, ensuring maximum power transfer, despite wind speed variation and system uncertainty. To specify the optimal trajectory to follow, a TS reference model is proposed taking as input the optimal rectified DC current. The conditions of stability and stabilization are expressed in terms of linear matrix inequality (LMI) for uncertain and disturbed T-S models leading to determining the controller gains. Finally, an example of MPP tracking applied to a Wind Energy Conversion System (WECS) illustrates the effectiveness of the proposed fuzzy control law. [ABSTRACT FROM AUTHOR]

Summary Problems like intermittent photovoltaic (PV) power, disconnected PV arrays, etc. are brought on by the extensive integration of PV generation. In this article, a single‐stage PV‐integrated grid‐tethered system is controlled using a reconfigurative multiple random features kernel mean p‐power (MRFKMP) control algorithm. The algorithm circumvents the dependence of kernel parameter in the classic random Fourier features mapping and lower the computing complexity in the traditional multiple kernel learning adaptive filter. MRFKMP algorithm with a linear filter structure can reduce both the computational and storage cost. This is the novel application of MRFKMP algorithm to extract fundamental component of nonlinear load currents in PV‐integrated grid‐tied system. Furthermore, to increase the reliability of the system, a reconfigurable control is developed, which adaptive regulates DC link voltage from PV mode (PVM) during PV hours to active power filtering mode (AFM) during PV nonavailability/PV disconnection. On a created laboratory testbed, the applied control's practical feasibility for numerous scenarios is evaluated. Comparison with existing control algorithms reveals that the MRFKMP controller outperforms the LMF based PV system by 79% less peak‐to‐peak load current ripple and 60% less than the best performing SRF control algorithm. The grid current THD is 2.60%, which is 30% low than that presented by SRF technique. [ABSTRACT FROM AUTHOR]

The continuous change in the environmental conditions has affected the solar panel output power. The ultimate goal is to keep calculated power close to actual maximum power, having minimal steady-state oscillations under continuous change in operating conditions such as irradiances and temperature. Therefore, this paper proposes a new hybrid technique based on an artificial neural network (ANN) and fractional-order PID (FOPID) controller. The sanitized teacher learning-based optimization algorithm is utilized for training the feed-forward ANN using the experimental data. Then, the water cycle algorithm is employed to tune the parameters of the FOPID controller. To examine the performance of the proposed technique, two different solar panels are taken for the study under various operating conditions, cloudy conditions, and partial shading conditions. The proposed technique is also tested for different array configurations of the solar panel. It is noticed that the proposed hybrid technique demonstrates better results in terms of various performance indices such as maximum power, efficiency recorded close to 99.99%, relative error recorded to be 0.000549, and minimum tracking speed recorded to be 0.059 s as compared to latest published work in the literature. [ABSTRACT FROM AUTHOR]

AbstractThis work presents an improvement of the incremental conductance technique by adding PI controllers optimized by the recent equilibrium optimization algorithm. It addresses the main weaknesses of the incremental conductance algorithm consisting of the choice of step-size and the elimination of oscillations around the MPP. The proposed approach makes the MPPT control more dynamic and self-adaptive against the atmospheric conditions change. It has been applied for a photovoltaic generator, the thermoelectric generator and for the hybridization of the two generators. The simulation results, and the comparative study with the optimized step-size P&O and Incremental conductance methods, prove the efficiency and superiority of the proposed technique in terms of energy extraction and speed. The introduced work is developed in the MATLAB/Simulink environment. [ABSTRACT FROM AUTHOR]

This article describes the multi-objective spotted hyena optimization (MOSHO) based control of a three-phase three-wire grid-tied dual-stage PV system. The MOSHO delivers an optimized step size $ (\boldsymbol{\delta }) $ (δ) for the incremental conductance (InC) based maximum power point tracking (MPPT) algorithm, as well as optimizes the PI controller gains for DC bus voltage $ ({{{\mathbf V}_{{\mathbf dc}}}}) $ ( V d c ) regulation. The $ \boldsymbol{\delta } $ δ optimized MPPT facilitates the faster and more accurate tracking of maximum power point with fewer fluctuations at steady-state. The MOSHO optimized PI controller regulates the $ {\textrm{V}_{\textrm{dc}}} $ V dc and delivers better stability with reduced $ {\textrm{V}_{\textrm{dc}}} $ V dc variations in DC bus voltage during steady-state and dynamic states, i.e. irradiation variation, abnormal and unbalanced grid voltage. The optimized DC bus generates an accurate loss component of current, which further enhances the voltage source converter (VSC) performance in terms of precise reference currents generation and power quality improvement. The proposed system performs satisfactorily in a MATLAB simulation environment as per IEEE519 standards. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel direct maximum power point tracking (MPPT) approach that combines the backstepping controller (BSC) and the super-twisting algorithm (STA). The direct backstepping super-twisting algorithm control (BSSTAC) MPPT was developed to extract the maximum power point (MPP) produced by a photovoltaic (PV) generator connected to the battery through a boost DC-DC converter. To reduce the number of sensors required for the BSSTAC implementation, a high gain observer (HGO) was proposed to estimate the value of the state of the PV storage system from measurements of the PV generator voltage and current. The suggested technique is based on the quadratic Lyapunov function and does not employ a standard MPPT algorithm. Results show that the suggested control scheme has good tracking performance with reduced overshoot, chattering, and settling time as compared to the prevalent MPPT tracking algorithms such as perturb and observe (P&O), conventional sliding mode control (CSMC), BSC, and integral backstepping controller (IBSC). Finally, real-time findings using the dSPACE DS 1104 software indicate that the generator PV can accurately forecast the MPP, as well as the efficacy of the suggested MPPT technique. The provided approach's effectiveness has been validated by a comprehensive comparison with different methods, resulting in the greatest efficiency of 99.88% for BSSTAC. [ABSTRACT FROM AUTHOR]

Partial shading significantly impacts the output power of photovoltaic (PV) arrays. This issue can be addressed by reconfiguring connections or positions of modules within the PV array. However, reconfiguration approaches present some drawbacks including the requirements of manual effort, complex algorithms, difficulty in implementation, and switching matrices that increase power losses. To overcome these issues, we present a low-cost and easily implemented reconfiguration method with integration into a maximum power point tracking (MPPT) algorithm for a PV array. We denote our MPPT approach as reconfigurable MPPT (RMPPT), and verify its applicability using a particle swarm optimization (PSO) algorithm, namely the reconfigurable PSO. Consequently, this approach has enhanced the output power of the PV array by at least 7.2% compared to PNO (perturb and observe) and PSO-based MPPT under realistic weather conditions. [ABSTRACT FROM AUTHOR]

This research presents a new method for controlling the maximum power point tracking (MPPT) of solar photovoltaic (PV) systems that are partially shaded. The proposed approach uses a neural network and an adaptive terminal sliding mode controller (NN-ATSMC) to ensure that the PV system operates at optimal performance under uncertain conditions. The NN-ATSMC controller is applied to a DC/DC boost converter to drive the system to the maximum power point (MPP). This method ensures that the error will converge in finite time and the chattering effect will be minimized without losing robustness under various disturbances and load conditions. Simulation results show that the proposed NN-ATSMC controller performs better than other types of controllers existing in the literature, such as a sliding mode controller (SMC) and a conventional proportional-integral controller (CPI). For the validation of the proposed controller, control hardware-in-the-loop (C-HIL) experimental implementation has been carried out through Texas Instruments digital signal processor C2000. The experimental results show the viability of real-time implementation and verify the effectiveness of the proposed method, which ensures the low cost and stability of the standalone PV systems. [ABSTRACT FROM AUTHOR]

In this paper, new methods for increasing the efficiency of photovoltaic pumping systems are presented (PVPS). A feasible implementation of battery-free PVPS, as well as a cost-effective design, has been proposed. The variation of the PV power causes its behaviour to transit permanently between the characteristics of constant current sources and constant voltage sources, which is studied in this paper from a new perspective. The inconsistency of PV generator behaviour reveals an unexpected phenomenon in the operation of induction motors (IM). To overcome the effects of fluctuating PV behaviour on IM operation, a modified fractional order MPPT controller (FO-MPPT) has been proposed. FO-MPPT has improved as a result of the use of several metaheuristics approaches, including the Grey Wolf (GWO), Anti-lion (ALO), and Whale Optimizer (WOA). A comparison of the proposed FO-MPPT and conventional MPPT techniques is carried out. The steady-state power, rise time, and efficiency are used as three measures to demonstrate the reliability of the proposed controller, demonstrating that the FO-MPPT outperforms traditional MPPT techniques. The optimal FO-ability MPPT's to regulate nonlinear and unpredictable dynamic loads is demonstrated. [ABSTRACT FROM AUTHOR]

Abstract This article incorporates grid-interfaced dual-stage solar photovoltaic (SPV) three-phase topology. Also, it employs an incremental conductance (INC) approach to extract the required maximum power from the PV array with the assistance of a DC-DC boost converter. In the next stage, an optimal control algorithm that exercises adaptive-based zero-attracting quaternion-valued sparse least mean square (ZA-QLMS) to control the voltage source converter (VSC). The proposed control technique enhances the convergence rate by including sparsity knowledge about the system in the weight-updation process. Thus, it allows the successful withdrawal of the fundamental current components that match the sensed grid current to produce requisite switching pulses for VSC. A 100 KW SPV system has been integrated with the utility grid through a VSC in MATLAB/Simulink environment. The feasibility of the ZA-QLMS-based control algorithm dictates that under variable irradiances and constant temperatures considering linear and non-linear loading situations, the generated power is effortlessly fed to the utility grid with the advantage of unity power factor operation. Thus, ensuring improved power quality performance with the total harmonic distortion of utility grid current is maintained well below the desired grid code (IEEE-519). [ABSTRACT FROM AUTHOR]

Nowadays, the modern world has more concern about increasing population as well as environmental changes. Hence using the Renewable Energy Sources (RES), power generation systems have actively developed in many countries. One of the main sources of RES is solar energy. In solar energy system, emitted energy from the sun is directly converted to electricity in Photovoltaic (PV) system. This research focuses on the advanced techniques based Maximum Power Point Tracking (MPPT) on grid connected and standalone solar PV applications. In this presents the MPPT of standalone solar PV system with Luo converter. Here, to authenticate the performance of the MPPT system with Luo converter, an incremental conductance technique of SPWM and SVM are used. The comparison of maximum power point tracking of integrated PV system with Sinusoidal Pulse-Width Modulation (SPWM) and Space Vector Pulse Width Modulation (SVPWM) techniques. The SVM performance is greater than the SPWM technique and also SVM technique provides better result than the SPWM. Here, Man of League Algorithm (MLA) is presented for the purpose of preserving the dc voltage of the system. [ABSTRACT FROM AUTHOR]

Photovoltaic panels present an economical and environmentally friendly renewable energy solution, with advantages such as emission-free operation, low maintenance, and noiseless performance. However, their nonlinear power-voltage curves necessitate efficient operation at the Maximum Power Point (MPP). Various techniques, including Hill Climb algorithms, are commonly employed in the industry due to their simplicity and ease of implementation. Nonetheless, intelligent approaches like Particle Swarm Optimization (PSO) offer enhanced accuracy in tracking efficiency with reduced oscillations. The PSO algorithm, inspired by collective intelligence and animal swarm behavior, stands out as a promising solution due to its efficiency and ease of integration, relying only on standard current and voltage sensors commonly found in these systems, not like most intelligent techniques, which require additional modeling or sensoring, significantly increasing the cost of the installation. The primary contribution of this study lies in the implementation and validation of an advanced control system based on the PSO algorithm for real-time Maximum Power Point Tracking (MPPT) in a commercial photovoltaic system to assess its viability by testing it against the industry-standard controller, Perturbation and Observation (P&O), to highlight its advantages and limitations. Through rigorous experiments and comparisons with other methods, the proposed PSO-based control system's performance and feasibility have been thoroughly evaluated. A sensitivity analysis of the algorithm's search dynamics parameters has been conducted to identify the most effective combination for optimal real-time tracking. Notably, experimental comparisons with the P&O algorithm have revealed the PSO algorithm's remarkable ability to significantly reduce settling time up to threefold under similar conditions, resulting in a substantial decrease in energy losses during transient states from 31.96% with P&O to 9.72% with PSO. [ABSTRACT FROM AUTHOR]

This article presents a comprehensive review of reduced device count multilevel inverter (RDC MLI) topologies for PV systems. Multilevel inverters are widely used in medium-voltage and high-power applications such as wireless power transform applications, flexible AC transmission (FACT), active filters, AC motor drives, high-voltage DC transmission (HVDC), and renewable energy sources due to their high modularity and high-power quality output. Multilevel inverters have the ability to diminish the harmonics content in the output voltage by applying various modulation techniques. The literature in this field showed that the high-power quality and high modularity of the output demand an undeniable need for multilevel inverter topology. Research in this field has identified various multilevel inverter topologies, each possessing their own merits and demerits. The ubiquitous availability of multilevel inverter topologies illustrates the complexity of their accurate selection. To avoid such complexity, this review shows the state of the art of various reduced device count (RDC) multilevel inverter (MLI) topologies. Details of the various RDC MLIs, along with their comparisons, are provided in this paper. This review will be an important reference tool for future work on RDC MLI for photovoltaic (PV) systems. [ABSTRACT FROM AUTHOR]

The paper makes a comparative study of the four Maximum Power Point Tracking (MPPT) algorithms used for Partial Shading Condition, Conservative MPP, Fast Terminal Sliding Mode (FTSM) controllers and under consistent shading circumstances. In addition, ANN, Kinetic Gas Molecule Optimization (KGMO) and FTSM MPPT are also included. A smart method is to use FTSM GWO algorithms under partly shadowed situations on a global MPP. The study includes a GWO methodology that successfully sets the efficient FTSM controller parameters such that the global maximum PV device powerpoint is monitored under partial shade. In the MATLAB setting and test performance, it is programmed for the suggested methodology of changing shade patterns dynamically. The findings are assessed and compared with ANN, KGMO and FTSM algorithms, and a more accurate method of monitoring the global MPP is found. The methods are also evaluated and contrasted in a PV array under various partial shadowing circumstances. Experimental effects are used to verify the control technique and stability analysis is performed based on the nonlinear method of analysis in the closed-loop design. [ABSTRACT FROM AUTHOR]

The output characteristics of photovoltaic (PV) array change with the environmental conditions and running state.In order to meet the control requirements of Maximum Power Point Tracking (MPPT) under different operation conditions, a segmented control method combining improved Quantum Particle Swarm Optimization (QPSO)and perturb and observe algorithm is proposed after analyzing the output characteristics of photovoltaic array under various working conditions.The inconsistent adaptive mutation DCWQPSO is used to search the maximum power point globally in the initial stage of tracking control to make the power point converge to the maximum power point quickly in order to improve the tracking speed, then the perturb and observe algorithm based on closed-loop fuzzy control is used to search the maximum power point locally to improve the tracking accuracy.The Matlab simulation results show that the segmented control method can complete MPPT in only 0.32 s under various working conditions of photovoltaic array and remains stable, which has faster tracking speed and higher tracking accuracy than others, indicating its capacity to improve the efficiency of PV generation effectively. [ABSTRACT FROM AUTHOR]

Standalone Microgrid has implemented in Mat-lab/Simulink platform, which has two Dispersed generation units based on a Solar Photovoltaic Generation and Wind Turbine Generation, how to get the most of wind and solar energy by lowering investment and operating expenses based on load power demand. The goal is to reduce one-time investment and operation expenses over the lifecycle; the limits are utilization rate and power supply reliability. The proposed system advantages, if Solar Photovoltaic Generation is absent, Wind Turbine Generation meets the load power demand, in case of a crucial situation, if Wind Turbine Generation is also absent, then Energy Storage System meets the load power demand, which means the power supply is reliable to the remote areas/limited load demand, besides in proposed system converters are reduced, so investment reduces. Mathematical models of Solar Photovoltaic Generation and Wind Turbine Generation are presented, to achieve Maximum Power Point Tracking of a Solar Photovoltaic Array, the Perturb and Observe method is used. Proportional-Integral controller used for Wind Turbine Generation, Solar Photovoltaic Generation, and Energy Storage System-based Direct Current/Direct Current Bi-directional Converter. An effective inductance filter is used for the mitigation of harmonics in the system. The finest simulation results are obtained, it concludes the system is in balanced condition under various loads with the Proportional-Integral controller and current total harmonic distortions are within limits. [ABSTRACT FROM AUTHOR]

An improved combined-mode power converter (CMPC) for a solar photovoltaic (SPV) system is presented in this paper. Ten-power electronic switches are utilised for the functioning of the converter. The proposed converter has four modes of operation: buck, boost, inverter, and mixed mode. The new CMPC topology provides a unique and complex switching sequence for implementing the mixed-mode operation. This work uses a field-programmable gate array (FPGA) for mode selection and to create PWM (MS-PWM) signals. A digital MS-PWM controller is configured to switch between improved stepped perturb and observe (ISPO) maximum-power point tracking (MPPT) and sine wave PWM (SPWM). The ISPO provides the appropriate duty cycle for buck/boost operation, while the SPWM controls the inverter operation. This work’s new digital PWM control algorithm supports mode selection, PWM generation, and reconfiguring PWM generation timings. The whole framework for constructing the MS-PWM controller employs a very high-speed integrated circuit hardware description language (VHDL). Simulation and experimental results are presented, proving that the digital MS-PWM controller uses fewer resources with improved accuracy. [ABSTRACT FROM AUTHOR]

The Proton Exchange Membrane Fuel Cells (PEMFCs) are one of the most effective and optimistic renewable energy source and they are extensively used in automotive applications. In the past, many researchers focused on solving the issues of extracting maximum power from fuel cell, controlling the speed and reducing the torque ripple of Brushless DC (BLDC) motor for fuel cell based Electric Vehicle (EV) systems. However, it is challenging to fine-tuning the gain parameters in the existing works MPPT approach and extracting maximum amount of energy. Additionally, it has limitations like unregulated voltage, problems of large overshoot, slow tracking speed, output power fluctuation, computational complexity and intricate modeling. Thus, the proposed work aims to create a revolutionary methodology called Unified Firefly Ersatz Neural Network (UFENN) - Maximum Power Point Tracking (MPPT). The UFENN is a kind of optimization-based machine learning technique that was created for efficiently optimizing the parameters to extract the maximum energy from the fuel cells. Furthermore, in order to control the output voltage with the least amount of power loss, an Interleaved SEPIC converter is also used in this work. During performance analysis, an extensive simulation results have been taken for validating the results of the proposed scheme by using various evaluation indicators. • Maximum power extraction from PEMFC. • An improved optimization based AI mechanism is developed for MPPT controlling. • The output voltage is highly boosted and regulated with the use of interleaved SEPIC converter. • An extensive simulation is carried out to assess the performance of the proposed model for electric vehicle application. • Enhancement of quality in electric power at EV systems. [ABSTRACT FROM AUTHOR]

Energy harvesting (EH) sources require the tracking of their maximum power point (MPP) to ensure that maximum energy is captured. This tracking process, performed by an MPP tracker (MPPT), is performed by periodically measuring the EH transducer's output at a given sampling rate. The harvested power as a function of the sampling parameters has been analyzed in a few works, but the power gain achieved with respect to the case of a much slower sampling rate than the EH source's frequency has not been assessed so far. In this work, simple expressions are obtained that predict this gain assuming a Thévenin equivalent for the EH transducer. It is shown that the power gain depends on the relationship between the square of AC to DC open circuit voltage of the EH transducer. On the other hand, it is proven that harvested power increases, using a suitable constant signal for the MPP voltage instead of tracking the MPP at a low sampling rate. Experimental results confirmed the theoretical predictions. First, a function generator with a series resistor of 1 kΩ was used, emulating a generic Thévenin equivalent EH. Three waveform types were used (sinus, square, and triangular) with a DC voltage of 2.5 V and AC rms voltage of 0.83 V. A commercial MPPT with a fixed sampling rate of 3 Hz was used and the frequency of the waveforms was changed from 50 mHz to 50 Hz, thus effectively emulating different sampling rates. Experimental power gains of 11.1%, 20.7%, and 7.43% were, respectively, achieved for the sinus, square, and triangular waves, mainly agreeing with the theoretical predicted ones. Then, experimental tests were carried out with a wave energy converter (WEC) embedded into a drifter and attached to a linear shaker, with a sinus excitation frequency of 2 Hz and peak-to-peak amplitude of 0.4 g, in order to emulate the drifter's movement under a sea environment. The WEC provided a sinus-like waveform. In this case, another commercial MPPT with a sampling period of 16 s was used for generating a slow sampling rate, whereas a custom MPPT with a sampling rate of 60 Hz was used for generating a high sampling rate. A power gain around 20% was achieved in this case, also agreeing with the predicted gain. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) energy, representing a renewable source of energy, plays a key role in the reduction of greenhouse gas emissions and the achievement of a sustainable mix of energy generation. To achieve the maximum solar energy harvest, PV power systems require the implementation of Maximum Power Point Tracking (MPPT). Traditional MPPT controllers, such as P&O, are easy to implement, but they are by nature slow and oscillate around the MPP losing efficiency. This work presents a Reinforcement learning (RL)-based control to increase the speed and the efficiency of the controller. Deep Deterministic Policy Gradient (DDPG), the selected RL algorithm, works with continuous actions and space state to achieve a stable output at MPP. A Digital Twin (DT) enables simulation training, which accelerates the process and allows it to operate independent of weather conditions. In addition, we use the maximum power achieved in the DT to adjust the reward function, making the training more efficient. The RL control is compared with a traditional P&O controller to validate the speed and efficiency increase both in simulations and real implementations. The results show an improvement of 10.45% in total power output and a settling time 24.54 times faster in simulations. Moreover, in real-time tests, an improvement of 51.45% in total power output and a 0.25 s settling time of the DDPG compared with 4.26 s of the P&O is obtained. [ABSTRACT FROM AUTHOR]

This paper proposes a fractional-order adaptive backstepping control (FOABC) with disturbance and uncertainty terms compensation for improving the MPPT (maximum power point tracking) performance and output power quality of a doubly fed induction generator (DFIG)-based wind turbine. In the proposed high-efficacy controller, disturbance and uncertainty terms are estimated in real time using an adaptive estimator designed using a recursion design process based on the nonlinear backstepping control. Meanwhile, a robust compensator is schemed and incorporated into the backstepping control algorithm so that it suppresses the effects of external disturbances and system uncertainties and, as a result, ensures the maximum wind energy extraction as much as possible, which is usually well known as MPPT. Furthermore, the fractional-order control approach was deployed in the proposed adaptive backstepping controller to provide a smooth control signal for enhancing the quality of the power injected into the grid. The stability analysis of the overall closed-loop system was performed using Lyapunov's stability theory. The high effectiveness of the proposed control method was assessed through simulation studies carried out in MATLAB/Simulink of a DFIG-wind turbine operating in various conditions. [ABSTRACT FROM AUTHOR]

Individual users are increasingly employing photovoltaic (PV) arrays on a commercial and small scale, and they are all attempting to obtain the maximum available power from the panels. The P-V characteristic of the PV module changes after every small-time duration because of the highly fluctuating atmospheric conditions. However, in such cases, it is indispensable to track the Maximum Power Point (MPP), and this becomes a strong nonlinear issue with a time-bounded solution. Therefore, this paper proposes a Logarithmic Seagull Artificial Neural Network-based Improved Perturb and Observe Maximum Power Point Tracking (LSANN-IPOMPPT) algorithm to acquire maximum power from the PV system. The temperature and irradiance are the input variables and the optimal current and voltages to trace the MPP are computed by using the LSANN method. Then, the IPOMPPT algorithm is built for the DC–DC converter, which functions as an interface connecting solar modules and the load for transferring maximum power. The DC–DC converter is tuned by an LSANN-IPOMPPT controller to exploit the solar array at a maximal power point. The tracking ability of the proposed LSANN-IPOMPPT is evaluated with current state-of-the-art approaches for differing irradiance and temperature levels. The simulation outcomes depicted that the implementation of LSANN-based IPOMPPT algorithm with a zeta converter exhibited a more efficient output power range than the existing methods. [ABSTRACT FROM AUTHOR]

In the search for efficient non-permanent magnet variable-speed wind generator solutions, this paper proposes a maximum power point tracking (MPPT) current-control method for a wound rotor synchronous wind generator. The focus is on direct-drive, medium-speed wind generators. In the proposed method, the currents of the wound rotor synchronous generator (WRSG) are optimally adjusted according to the generator speed to ensure maximum power generation from the wind turbine without needing information on wind speed. The design, modeling, and simulation of the MPPT current controllers are done in Matlab/Simulink with the WRSG in the synchronous reference frame. The controller is put to the test using different wind speed profiles between cut-in and rated speeds. The simulation results indicate that the proposed current control method is simple, effective, and robust, suggesting its practical implementation. To validate the simulation results, experimental work on a 4.2 kW WRSG prototype system is presented to demonstrate the stability and robustness of the MPPT current control method in operating the turbine at or near the maximum power point. [ABSTRACT FROM AUTHOR]

Solar energy-based Electric Vehicles (EVs) are becoming more popular in recent days due to their enormous profitability. The existing PV-based EV application systems comprise the major problems of increased computational complexity, reduced efficiency, incompetent speed control and high error rate. Thus, this research work intends to develop a novel controlling mechanism named Tuple Accelerated Batch Gradient Descent (TABGD) for enhancing the speed control of EVs with a high battery power support. The main contribution of this technique is to control the converter and inverter circuits by optimally tuning the parameters of the controlling mechanism. Also, it generates the switching control signal with regulated DC output and motor current. Moreover, an improved Batched Bee Colony Optimization (BBCO) algorithm is employed to provide optimal battery power support to the EV system. The experimental results validate the performance of the proposed mechanism and compare it with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The solar photovoltaic (PV) sources have low voltage output and hence cannot be used directly in micro‐grid applications. Therefore, to boost the low voltage output, a novel high gain single‐ended primary inductor converter (SEPIC) is proposed in this manuscript. The proposed high gain SEPIC (HGS) has an advantage of obtaining high voltage gain at a low duty ratio and continuous input current, which makes it viable for PV applications. The proposed converter offers simple control, and the high gain is obtained without the use of any transformer or coupled inductor so the semiconductor switch will not experience a voltage overshoot during the turn‐off operation. Hence, the conduction losses of the converter switch are reduced, and the performance of the converter is enhanced. Additionally, this manuscript also investigates the effectiveness of the proposed HGS in integration with the PV array. An extensive performance evaluation of the proposed HGS is investigated and compared with different SEPIC topologies in irradiance change conditions. Experimental implementation of the proposed HGS confirms the theoretical analysis and verifies the performance. Also, the real time (RT) analysis of the proposed HGS with PV array is conducted to show the validation of high gain operation for the PV application. [ABSTRACT FROM AUTHOR]

Maximum power point tracking (MPPT) through an effective control strategy increases the efficiency of solar panels under rapidly changing atmospheric conditions. Due to the nonlinearity of the I–V characteristics of the PV module, the Sliding Mode Controller (SMC) is considered one of the commonly used control approaches for MPPT in the literature. This paper proposed a Backstepping SMC (BSMC) method that ensures system stability using Lyapunov criteria. A fuzzy inference system replaces the saturation function, and a modified SMC is used for MPPT to ensure smooth behavior. The proposed Fuzzy BSMC (FBSMC) parameters are optimized using a Particle Swarm Optimization (PSO) approach. The proposed controller is tested through various case studies on account of MPP's dependence on temperature and solar radiation. The controller performance is assessed in partial shading conditions as well. The simulation results show that less settling time, a small error, and enhanced power extraction capability are achieved by applying the PSO-based FBSMC approach compared to the conventional BSMC- and ABC-based PI control presented in previous research in different scenarios. Moreover, the proposed approach provides faster adaptation to temperature and solar radiation variation, ensuring faster convergence to the MPP. Finally, the robustness of the proposed controller is validated by providing variation within the system components. The result of the proposed controller clearly indicates the lowest value of RMSE measured between PV voltage and the reference voltage, as well as the RMSE between PV power and maximum power. The results also show that the proposed MPPT controller exhibits the highest dynamic efficiency and mean power. [ABSTRACT FROM AUTHOR]

Recently, controlling a wind energy conversion system (WECS) under fluctuating wind speed and enhancing the quality of power delivered to the grid has been a demanding challenge for many researchers. This paper provides a comprehensive review of synchronous generator-based WECSs. This paper will investigate the growth of wind energy in Egypt and throughout the world, as well as the technological and financial significance of wind energy. The block diagram of a typical grid-connected WECS, power control techniques, characteristic power curve-based maximum power point tracking (MPPT), and MPPT techniques are also presented in this study. Moreover, this study compares different power converter topologies for grid-connected and independent WECSs that use a permanent magnet synchronous generator (PMSG). [ABSTRACT FROM AUTHOR]

The maximum power point tracking concept is essential in PV systems to increase the power transfer capability and efficiency. The conventional methods to track maximum power point perform satisfactorily only under uniform solar irradiation, however under partially shaded conditions, PV array offers several local maxima along with one global maximum power transfer point on the P-V curve. This makes optimal tracking more difficult. This paper proposed an improved adaptive MPPT algorithm with varying step size to track the Global Maximum Power Point (GMPP) under different partial shaded conditions. First, the simulation model has been analyzed which is suitable for partially shaded conditions and then the proposed Variable Step-size Adaptive Maximum Power Point Tracking (VSAMPPT) method has been developed to track the global optimal power point for various irradiations. Here changing the duty cycle is not with fixed step size but with variable step size (Dstep), which depends on the square of the difference between the ratio of voltages to current and makes the proposed algorithm faster. The proposed algorithm keeps on adjusting its tracking strategy to find Global Peak Area (GPA) where the global maximum power point can be tracked down very easily. The proposed algorithm has been validated in the laboratory with dSPACE (DS1103) and tested with different case studies. The results demonstrate the effectiveness of the proposed concept over the existing MPPT methods like P&O method, Incremental Conductance (IC), and Adaptive MPPT methods in terms of speed of tracking global maximum power point, accuracy, and flexibility. [ABSTRACT FROM AUTHOR]

Recently, the concept of green building has become popular, and various renewable energy systems have been integrated into green buildings. In particular, the application range of fuel cells (FCs) has become widespread due to the various government plans regarding green hydrogen energy systems. In particular, proton exchange membrane fuel cells (PEMFCs) have proven superiority over other existing FCs. However, the uniqueness of the operating maximum power point (MPP) of PEMFCs represents a critical issue for the PEMFC control systems. The perturb and observe, incremental conductance/resistance, and fuzzy logic control (FLC) represent the most used MPP tracking (MPPT) algorithms for PEMFC systems, among which the FLC-based MPPT methods have shown improved performance compared to the other methods. Therefore, this paper presents a modified FLC-based MPPT method for PEMFC systems in green building applications. The proposed method employs the rate of change of the power with current ( d P / d I ) instead of the previously used rate of change of power with voltage ( d P / d V ) in the literature. The employment of d P / d I in the proposed method enables the fast-tracking of the operating MPP with low transient oscillations and mitigated steady-state fluctuations. Additionally, the design process of the proposed controller is optimized using the enhanced version of the success-history-based adaptive differential evolution (SHADE) algorithm with linear population size reduction, known as the LSHADE algorithm. The design optimization of the proposed method is advantageous for increasing the adaptiveness, robustness, and tracking of the MPP in all the operating scenarios. Moreover, the proposed MPPT controller can be generalized to other renewable energy and/or FCs applications. The proposed method is implemented using C-code with the PEMFC model and tested in various operating cases. The obtained results show the superiority and effectiveness of the proposed controller compared to the classical proportional-integral (PI) based d P / d I -based MPPT controller and the classical FLC-based MPPT controller. Moreover, the proposed controller achieves reduced output waveforms ripple, fast and accurate MPPT operation, and simple and low-cost implementation. [ABSTRACT FROM AUTHOR]

Under partial shading conditions, the photovoltaic (PV) array will induce several local maximum power points, such that the traditional maximum power point tracking (MPPT) methods will trap in false maximum power points and reduce the energy conversion efficiency of the solar system. To solve this problem, this paper firstly introduces a multi-module solar energy system, where a master boost converter is used as a voltage regulator for the DC load, while other slave boost converters in parallel perform MPPT to support the load power as high as possible. Furthermore, a hybrid MPPT method is proposed by combining the shuffled frog leaping algorithm (SFLA) and the incremental conductance (In-Cond) method. The hybrid SFLA method can successfully and quickly find the maximum power region, while the exact MPP is achieved in the incremental conductance searching phase. As a result, trapping local maximum power points is avoided, while the computing complexity is reduced. Finally, simulation and experiments are carried out to verify the validity of the proposed MPPT method under partial shading conditions. [ABSTRACT FROM AUTHOR]

The article presents an induction generator connected to the power grid using the AC/DC/AC converter and LCL coupling filter. Three-level inverters were used in the converter, both from the generator side and the power grid side. The algorithm realizing Pulse Width Modulation (PWM) in inverters has been simplified to the maximum. Control of the induction generator was based on the Direct Field-Oriented Control (DFOC) method. At the same time, voltage control has been used for this solution. The MPPT algorithm has been extended to include the variable pitch range of wind turbine blades. The active voltage balancing circuit has been used in the inverter DC voltage circuit. In the control system of the grid converter with an LCL filter, the number of measurements was limited to the measurement of power grid currents and voltages. Synchronization of control from the power grid side is ensured by the use of a PLL loop with the system of preliminary suppression of undesired harmonics. [ABSTRACT FROM AUTHOR]

This paper presents an experimental validation of the Extremum Seeking and Sliding mode-based control for an autonomous PV system under uniform and non-uniform irradiance conditions. The modeling of the PV system and the control MPPTs adopted in this study are fully formulated and then implemented via a DSpace 1104 single card. The experimental setup comprises two cases: a healthy state (case 1) and a shaded state with two selected ratios (15% and 5%), which have been used to carry out the results. The obtained results show notable skills of the ESC-based strategy to extract the maximum power under shaded conditions for all the proposed scenarios. [ABSTRACT FROM AUTHOR]

In this article, the Wiener variable step size with variance smoothening (WVSSV) technique is introduced for the utility supportive solar photovoltaic (PV) system. This system performs the function of power factor correction, compensation of nonactive component, and reduction of total harmonics distortion (THD) from the utility grid. The dc–ac converter is used in between the PV array and the common connection point and it is controlled through the WVSSV technique to produce the switching signals. The WVSSV technique presents impressive robustness and good adaptation in highly adverse nonlinear load conditions by estimating the clean and harmonic less fundamental component. The presented technique has a good convergence rate even at the starting of adapting and the step size is adjusted to eliminate the variety of noise and harmonics of nonlinear loads. The efficacy of the WVSSV control technique is presented through simulated and test results and depicts the improved steady-state as well as dynamic performances of the PV system. The THDs of grid parameters are found under the IEEE-519 standard in different working conditions. [ABSTRACT FROM AUTHOR]

A novel technique is presented for Maximum Power Point Tracking (MPPT) based photovoltaic (PV) system in partial shadow conditions for harvesting maximum power. In this paper, a hybrid technique is developed, which combines Black Widow Optimization (BWO) with Recurrent Neural Network (RNN). To train the data set and provide a control signal for the converter, an RNN is used. After fitting the training data sets, the suggested method achieved maximum power by utilizing BWO based on the control parameters. This proposed method minimizes the difference between actual and average power. Using an optimization technique, the main goal of this proposed strategy is to obtain peak power harvest under various conditions, including partial shading, while minimizing error function, With the help of MATLAB/Simulink software, the conclusions are revealed under various partial shading conditions. For each category, the observed results are evaluated at various time intervals. The proposed method is also compared to other techniques such as the Ant Colony Optimization (ACO)-RNN system, Particle Swarm Optimization (PSO)-RNN system, and Gravitational Search Algorithm (GSA)-RNN system. The proposed system is 36.11% faster than GSA with RNN, 39.47% faster than PSO, and 42.5% faster than ACO with RNN in terms of tracking speed. Significantly, the proposed work is 0.87% more efficient than the other models in terms of obtaining maximum power. In terms of obtaining maximum power, the proposed work BWOA-RNN is more effective than other methods. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) panel produces electricity depending on a variety of characteristics, including the PV module model, design specifications, and ambient circumstances such as temperature and sun irradiation. To analyze and model the effect of these factors on PV performance, a PV model is significant to be studied and modeled in advance. It is desirable to be compatible with the real-physical behavior of the PV panel. This paper presents mathematical modeling, design, and simulation of the three-diode model (3DM) MPPT controller instead of using conventional single/double diode PV models. The proposed PV model is analyzed, verified, and simulated at various temperature and irradiance levels. Furthermore, Particle Swarm Optimization (PSO) as a multi-objective algorithm is used for the Maximum Power Point Tracking MPPT controller to enhance the performance of the module and PV array system. A DC/DC boost converter is combined with the proposed 3DM model and connected through a resistive load. Results show that adopting PSO-based MPPT improves the performance of the PV panel compared to the traditional MPPT and verified the theoretical background. [ABSTRACT FROM AUTHOR]

It is challenging to obtain maximum power output from a Solar Photovoltaic System (SPVS) under uncertain weather conditions due to rapid change in temperature and irradiance. It is achievable through an application of an appropriate Maximum Power Point Tracking (MPPT) technique. The precise tracking of Maximum Power Point (MPP) is crucial for randomly varying irradiance and temperature due to sudden occurrence of clouds and rain during uncertain variable weather conditions. In this paper, an approach based on Intersection Point Determination Method (IPDM) is proposed to effectively track the MPP under such uncertain climatic conditions. It is an iterative method which starts with formation of a rectangle by drawing straight lines parallel to voltage and current axes of I–V curve, subsequently identifies the point of intersection of its diagonals. This identified intersection point is used as the initial point for the next iteration until the convergence is achieved i.e. the new intersection point superimposes the previous point in two successive iterations. The computer simulations have been carried out on a stand-alone (MSX-60) solar PVS of 60W along with a boost converter designed in MATLAB/Simulink. The performance of proposed IPDM technique is tested and compared with other well established MPPT techniques in four distinct theoretical test cases comprises all possible variable weather conditions to ensure its vast applicability. Further, an experimental setup is established in laboratory environment to also validate its performance for the real-life input variables. In which, the maximum and average power outputs are achieved to 50.5598 W and 30.6338 W respectively, for the unfavourable real-life weather conditions i.e. mostly a cloudy day with rainfall and sunshine for short durations. All these simulation results as obtained by all five MPPT methods are statistically validated through t-Tests with 5% of confidence of interval. The statistical analysis proves the superiority of the proposed IPDM technique as compared to the other four competing MPPT techniques. The proposed method is promising in terms of better convergence, fast tracking speed, negligible oscillations, and maximum power output in all possible weather conditions. [ABSTRACT FROM AUTHOR]

• A novel MPPT controller based on yellow saddle goatfishes algorithm (YSGA) for energy optimization of PV systems. • Qualitative and quantitative comparison of YSGA with BAT,GWO,SOA,PSO, and P&O under partial shading condition. • Statistical analysis based on: the average best so far (AB), the median best so far (MB), and standard deviation (SD). • Wilcoxon test with 5% significance level based on the median value is applied to verify the superiority of the algorithm. This paper proposes a novel maximum power point tracking (MPPT) technique based on a new optimization algorithm called yellow saddle goatfish algorithm (YSGA). The photovoltaic system loses a significant amount of power under partial shading conditions (PSC). Because of the presence of bypass diodes, several maximums appear in the Power-Voltage characteristic, making tracking the global maximum power point (GMPP) extremely difficult. A yellow saddle goatfish-based MPPT algorithm is proposed and implemented to address this issue and improve the efficiency of the PV system. This algorithm employs a subgroup research methodology and a very interesting strategy of particle collaboration during GMPP tracking, providing a balance between the exploration and exploitation phases of the research process. To investigate its performance, tests were conducted using six different partial shading patterns and a fast irradiance variation. The simulation was carried out in MATLAB/Simulink using a series of MSX60 solar panels. The results were compared to well-known and efficient algorithms such as the Grey wolf optimization algorithm (GWO), the Bat algorithm (BAT), particle swarm optimization (PSO), the Seagull optimization algorithm (SOA), and the Perturb and observe (P&O) algorithm. The findings demonstrate the proposed method's high robustness, as well as its high dynamic and accuracy in tracking the GMPP. Furthermore, a quantitative and statistical analysis of 100 tests revealed that it outperformed the other algorithms in terms of efficiency, success rate, convergence time, and solution dispersion. [ABSTRACT FROM AUTHOR]

Wind turbines driven by permanent magnet synchronous generators (PMSG) are widely used in the present utility industry because of their better efficiency in terms of energy generation and design simplicity. However, the performance of such a system is greatly reduced when subjected to the intermittency of a wind speed or sudden wind gust. An efficient approach was proposed by integrating the neural network (NN)-based wind turbine model with a PMSG system to accurately and efficiently estimate the maximum power and optimal turbine speed to drive the PMSG rotor. This strategy employs the speed control method for the PMSG system, where the proportional-integral controller is feedback with the error signal of the PMSG output rotor speed and the reference speed generated by the NN-based wind turbine model. The performance of the wind turbine NN-based PMSG model was investigated using both MATLAB and SIMULINK tools. The robustness of the control strategy is verified by subjecting the model to the wind speed ranging from 4 to 13 m/s to obtain the maximum power with an error as low as 0.0025%. Further, a case study was performed by using the real-time wind speed data for Hafar Al-Batin, KSA and the potential for wind power generation is explored. The maximum power and optimum reference speed to drive the PMSG rotor was estimated using the proposed model for this location and compared with the theoretically calculated maximum power and optimum reference speed with an error of less than 0.27%. [ABSTRACT FROM AUTHOR]

The gradual displacement of conventional generation from the energy mix to give way to renewable energy sources represents a paradigm shift in the operation of future power systems: on the one hand, renewable technologies are, in general, volatile and difficult to predict; and on the other hand, they are usually connected to the grid through electronic power converters. This decoupling due to power converters means that renewable generators lack the natural response that conventional generation has to the imbalances between demand and generation that occur during the regular operation of power systems. Renewable generators must, therefore, provide a series of complementary services for the correct operation of power systems in addition to producing the necessary amount of energy. This paper presents an overview of existing methods in the literature that allow photovoltaic generators to participate in the provision of ancillary services, focusing on solutions based on power curtailment by modifying the traditional maximum power point tracking algorithm. [ABSTRACT FROM AUTHOR]

DC-DC power converters play an important role in the performance and control methods of solar power systems. Solar power converters are prone to high power losses due to intermittent irradiation and shading effects on solar photovoltaic (PV) modules. The device topology and control algorithm of a solar power converter are key factors to increase the total amount of harvested energy. In this paper, a solar power converter is proposed with a two−phase interleaved boost converter (IBC) topology and a novel maximum power point tracking (MPPT) method. The proposed MPPT controller is based on extended Kalman filtering (EKF) and it improves the tracking efficiency in both steady irradiation and partial shading conditions. The algorithm is improved with the prediction and estimation capabilities of the EKF algorithm. The proposed EKF MPPT is validated with simulations and experimentally validated by using the implemented two−phase IBC that is comprised of SiC MOSFETs. The proposed converter provides over 99% power conversion efficiency at 3 kW and over 96% MPPT tracking efficiency under partial shading conditions. The experimental studies verify that the proposed MPPT controller and two−phase IBC increase the overall efficiency both in steady-state and partial shading operations of a solar power converter. [ABSTRACT FROM AUTHOR]

This article presents a photovoltaic (PV)–battery energy storage (BES) system functioning in both grid-tied and standalone modes while performing multifunctional operations, including reactive power compensation, power balancing, and power quality enhancement. The PV–BES system ensures uninterrupted power supply to the critical loads even during seamless transitions from grid-tied mode to islanded mode, and vice versa. The incremental conductance technique controls the boost converter and confirms maximum PV power extraction. The bidirectional converter connects BES to the dc bus. The dc bus voltage $({{V}_{dc}})$ is regulated by a slime mold optimized proportional-integral controller to limit the dynamic state variation and improve stability. The voltage-source converter (VSC) is controlled by a minimum kernel-risk-sensitive mean p-power loss algorithm during the grid-synchronization mode. In contrast, the voltage control algorithm executes islanded control of VSC. The VSC control transition is achieved seamlessly without any significant disturbance at the point of common coupling. The hardware test results demonstrate that the present system remains stable during dynamic conditions and performs adequately as per the IEEE 519-2014 standard. [ABSTRACT FROM AUTHOR]

In the present paper, a current sensorless (CSL) method for buck-boost converter control is proposed for maximum power point tracking (MPPT) photovoltaic applications. The proposed control scheme uses the mathematical model of the buck-boost converter to derive a predefined objective function for the MPPT control. The proposed scheme does not require any current sensor and relies only on the input voltage signal, which decreases the implementation cost. The proposed method is successfully implemented using a Matlab/Simulink/Stateflow environment, and its effectiveness is compared over the perturb and observe (P&O) method. An experimental rig, that includes a buck-boost converter, a PV simulator, and a resistive load, is used for the experimental validation. A rapid Arduino prototyping platform is used for the digital implementation, where the SAM3X8E microcontroller of the Arduino DUE board, which integrates an ARM Cortex-M3 MCU, is used as a target hardware for the proposed model-based controller developed under the Stateflow environment. Furthermore, the integrated pulse width modulation (PWM) macrocell is used to generate accurate PWM gate-drive signals for the buck-boost converter. Compared to the P&O, the presented simulation and experimental results show that the proposed method has reduced the computation burden and the sensor cost of implementation by 24.3%, and 27.95%, respectively. [ABSTRACT FROM AUTHOR]