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A power processing system (PPS) with a seven‐level dual‐buck inverter (SLDBI) for a photovoltaic (PV) power generation system is proposed. The PPS is comprised of a boost power converter and an SLDBI. The boost power converter matches the voltage of the PV array with that of the SLDBI and functions as the maximum power point tracker for the PV array. The SLDBI consists of a dual‐buck bridge converter (DBBC) and a three‐level DC converter (TLDC). The DBBC effectively reduces leakage current in the PPS and prevents current from flowing through the parallel body diode of the power switches, allowing the use of MOSFETs as power electronic switches to enhance power efficiency. The TLDC is structured as a semi‐controlled full‐bridge system to generate a three‐level DC voltage. Besides, the DBBC and TLDC produce a seven‐level AC voltage and drive a sinusoidal current to the utility. The SLDBI reduces both the filter inductor size and the switching loss of power switches. With only six power switches, it simplifies the power circuit. A hardware prototype has been built to experimentally verify the proposed PPS. [ABSTRACT FROM AUTHOR]

Solar photovoltaic energy is a vital renewable resource because it is clean, endless, and pollution‐free. Due to the fast growth of the semiconductor and power electronics sectors, photovoltaic (PV) technologies are climbing significant attention in modern electrical power applications. Operating PV energy conversion systems at the maximum power point is essential for getting the maximum power output and raising efficiency. This paper proposes a regression‐based Perturb and Observe method to quickly find a global maximum power point, avoiding being stuck in local maxima, likewise analytical and metaheuristic methods. The improved control focuses on the narrowed search areas by linear and non‐linear regression analyses using the generated PV model on a flexible Python environment. Furthermore, the method's accuracy is validated in real time under variable temperatures, irradiations, and loads. This method was proven with a hardware implementation. The proposed method is more than 98% accurate and can withstand long‐term modelling. The suggested regression‐based perturbation and observation method provided a short learning time and easy implementation. Additionally, the dynamic recorded results can be visualized for researchers to utilize efficiently. [ABSTRACT FROM AUTHOR]

The pressing need for sustainable energy solutions has driven significant research in optimizing solar photovoltaic (PV) systems which is crucial for maximizing energy conversion efficiency. Here, a novel hybrid gazelle‐Nelder–Mead (GOANM) algorithm is proposed and evaluated. The GOANM algorithm synergistically integrates the gazelle optimization algorithm (GOA) with the Nelder–Mead (NM) algorithm, offering an efficient and powerful approach for parameter extraction in solar PV models. This investigation involves a thorough assessment of the algorithm's performance across diverse benchmark functions, including unimodal, multimodal, fixed‐dimensional multimodal, and CEC2020 benchmark functions. Notably, the GOANM consistently outperforms other optimization approaches, demonstrating enhanced convergence speed, accuracy, and reliability. Furthermore, the application of the GOANM is extended to the parameter extraction of the single diode and double diode models of RTC France solar cell and PV model of Photowatt‐PWP201 PV module. The experimental results consistently demonstrate that the GOANM outperforms other optimization approaches in terms of accurate parameter estimation, low root mean square values, fast convergence, and alignment with experimental data. These results emphasize its role in achieving superior performance and efficiency in renewable energy systems. [ABSTRACT FROM AUTHOR]

Parameter estimation of photovoltaic (PV) solar cells and module models pays attention to researchers owing to their importance in practical considerations. The single diode model (SDM) circuit with five unknown parameters is widely used to model PV solar cells and modules. In this paper, a novel approach called alternate optimization (AO) algorithm based on a discrete search is proposed to estimate the SDM parameters. The proposed algorithm provides efficient and robust performance, considering a limited set of discrete values and increasing the convergence speed. Two practical case studies with actual measurements are considered to assess the proposed AO algorithm: the RTC France solar cell and monocrystalline PV modules with different irradiations and temperatures. The numerical findings underscore the superior performance of the proposed AO algorithm across various metrics. Notably, it achieves an exceptional Root Mean Square Error (RMSE) of 7.7426 × 10−04 for the RTC France PV cell and approximately 1 × 10−03 RMSE for monocrystalline PV modules. Additionally, the algorithm exhibits unparalleled speed, showcasing the fastest convergence with an elapsed time of 1.66 × 10−05—markedly 4.45 times quicker than the fastest method documented in the literature for SDM parameter estimation. Furthermore, the proposed AO algorithm stands out for its efficiency, requiring a maximum of five iterations for parameter estimation, a substantial improvement compared to the more than 10 iterations typically needed by algorithms in the existing literature. Its robustness is also commendable, as evidenced by the stability of final RMSE values across a variety of experiments, distinguishing it from less robust algorithms found in the literature. [ABSTRACT FROM AUTHOR]

Flexible power point tracking (FPPT) is a common and significate photovoltaic control problem under partial shading conditions. Based on CPP and MPP distribution analysis, both constant power point tracking and maximum power point tracking could be mathematically described by a single objective optimization. As one of the recent meta‐heuristic algorithms, which avoid the limitations of conventional scanning methods, the carnivorous plant algorithm (CPA) solves non‐convex optimization problems with fast speed, high accuracy, and simple calculation. However, it exhibits several drawbacks such as inappropriate parameters, over‐searching, and feasible solution ignored issues. Therefore, a global FPPT strategy based on modified CPA (mCPA) is proposed, which includes a search objective integration, efficient non‐convex search space skipping strategy, and adaptive tuning parameters according to sensitivity analysis. By statistical experiments compared with eight popular meta‐heuristic algorithms, the mCPA can correctly converge to the global MPP under various scenarios with the highest accuracy of 99.0%. Meanwhile, the observations of dynamic experiments demonstrate the effectiveness of the proposed strategy in terms of fast time response (0.5–3 s) for global FPPT under various uniform and non‐uniform insolation conditions. [ABSTRACT FROM AUTHOR]

Flexible power point tracking (FPPT) is a common and significate photovoltaic control problem under partial shading conditions. Based on CPP and MPP distribution analysis, both constant power point tracking and maximum power point tracking could be mathematically described by a single objective optimization. As one of the recent meta‐heuristic algorithms, which avoid the limitations of conventional scanning methods, the carnivorous plant algorithm (CPA) solves non‐convex optimization problems with fast speed, high accuracy, and simple calculation. However, it exhibits several drawbacks such as inappropriate parameters, over‐searching, and feasible solution ignored issues. Therefore, a global FPPT strategy based on modified CPA (mCPA) is proposed, which includes a search objective integration, efficient non‐convex search space skipping strategy, and adaptive tuning parameters according to sensitivity analysis. By statistical experiments compared with eight popular meta‐heuristic algorithms, the mCPA can correctly converge to the global MPP under various scenarios with the highest accuracy of 99.0%. Meanwhile, the observations of dynamic experiments demonstrate the effectiveness of the proposed strategy in terms of fast time response (0.5–3 s) for global FPPT under various uniform and non‐uniform insolation conditions. [ABSTRACT FROM AUTHOR]

This paper focuses on risk‐averse‐based optimal operation of a grid‐connected hybrid energy system (HES) composed of photovoltaic (PV), diesel generator, and battery storage system (BSS). For this goal, information gap decision theory (IGDT) is used to model load demand uncertainty. The aim of the optimal operation is to minimize cost of PV/diesel/BSS by optimal determination of the power purchased from the electricity grid. Since in the risk‐averse strategy, load demand has an undesirable impact on the objective function, the decision maker attempts to maximize the uncertainty radius in a way that any deviation of the uncertain parameter leads to an objective function value which is not worse than the critical value. Over the case studies (considering different radiations), simulation results indicate that in the risk‐neutral strategy, at high, medium, and low radiations, the operation cost is 28.88, 36.10, and 42.63$, respectively. In the risk‐averse strategy, when the radiation is high, by increase of the deviation factor from 0.1 to 0.25, the optimal uncertainty radius increases from 6.98% to 15.72% (increase of around 125%) and the operation cost increases from 31.768 to 36.101$. When the radiation is low, by increase of the deviation factor from 0.1 to 0.25, the uncertainty radius increases from 8.64% to 16.9% (increase of around 96%) and the operation cost increases from 46.895 to 53.291$. [ABSTRACT FROM AUTHOR]

In estimating the parameters of the five unknown parameters Single‐Diode Model (SDM) of the solar photovoltaic (PV) model, a non‐linear equation for the PV cell current is typically utilized. Then, the error between the estimated current and measured current is computed using the objective function called Root‐Mean‐Square‐Error (RMSE). In order to compute the PV cell current in SDM, an iterative method built on the Lambert‐W function is presented in this study. Along with the Lamber‐W function, an optimization algorithm called Weighted Velocity‐Guided Grey Wolf Optimizer (WVGGWO) is used to identify the unknown lumped parameters of SDM of the cell and the module. The proposed WVGGWO is an updated version of the original Grey Wolf Optimizer (GWO). The position update of the GWO has been modified, and the weightage has been provided for the wolf hierarchy. Additionally, by emphasizing the lengthening of each leading wolf's steps towards the others in the earlier search while emphasizing the shortening of the steps while reaching the later iterations, WVGGWO improves both the exploration and exploitation of the original GWO. Four case studies are considered for testing the validity of the proposed algorithm along with the Lambert‐W function. The performance of the proposed approach is compared with seven other well‐known algorithms. The results demonstrate that the suggested approach produces better outcomes than many optimization algorithms. [ABSTRACT FROM AUTHOR]

Cascaded inverters can improve efficiency and reduce cost, so it is widely used in photovoltaic (PV) grid connected power generation system. However, aging or partial shielding of PV panels will lead to their different maximum power points (MPP). This paper proposes a decentralized control strategy for grid‐connected cascaded PV inverters without any communication, which is capable of integrating PV inverters of different capacities connected in series into the grid, and enable them to achieve maximum power point tracking (MPPT) independently. Compared with the traditional centralized control method relying on real‐time communication, the proposed scheme has advantages of improved reliability and decreased costs. The overall system stability is analysed, and the stability condition is derived as well. The Hardware‐In‐the‐Loop test results show that all the inverters in the cascaded system have their output frequency and voltage phase synchronized autonomously, and achieve MPPT independently even in the presence of variation of light condition. Thus, the feasibility of the proposed method is verified. [ABSTRACT FROM AUTHOR]

Series‐compensated transmission lines can lead to a sub‐synchronous resonance (SSR) phenomenon at high compensation levels, where the system lacks stability. Gate‐controlled series capacitor (GCSC) is widely used to damp the SSR. Here, an effective and new methodology for damping the SSR is presented. The proposed method aims to alleviate the SSR phenomenon based on estimating both the frequency and the maximum value of the voltage magnitude signal rather than using the Proportional Integral controller. The appropriate turn‐off angle corresponding to the maximum value of the voltage magnitude signal is applied to the GCSC on the condition that the estimated frequency of the voltage magnitude signal is within the range of the SSR frequency. If the estimated frequency of the voltage magnitude signal is out of the range of the SSR frequency, the steady‐state turn‐off angle (1130) is applied to the GCSC. The capability of the proposed method for damping the SSR under various compensation levels, various wind speeds, and sub‐synchronous control interaction (SSCI) is validated by time‐domain simulation and eigenvalue analysis. Compared to the presented latest method for SSR damping using GCSC, all the obtained results demonstrate that the oscillations converge faster when the proposed method is activated. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) is essential for global carbon neutrality, it is imperative to forecast PV generation accurately for power operation. With the rapid growth of distributed PV sites, a scalable cloud service tends to play a vital role in PV forecasting to address the increasing cost of computing resources and data subscriptions. Such a scheme creates a possibility to further enhance forecasting performance by re‐using forecasting model, data, and computing resources all in the cloud. In order to achieve this goal, this work proposes a multi‐site PV forecasting system design with a message queue (MQ) and stream computing engine, where a hybrid neural network model is trained and continuously updated using real‐time data. A performance benchmark with up to 60 sites served simultaneously was performed to verify the scalability of the stream computing based approach. Moreover, after incremental updating of the forecasting model, a decrease in normalized root mean square error and normalized mean absolute error of PV forecasting were observed, demonstrating that better short‐term forecasting accuracy was achieved. [ABSTRACT FROM AUTHOR]

As the grid‐connected interface device of wind and photovoltaic power generation, the voltage source converter (VSC) must pass the low voltage ride‐through (LVRT) test. However, existing literature demonstrates that under weak grid conditions, there is a possibility of severe fluctuations in the terminal voltage of VSC during transiting from the pre‐sag normal operation mode to the LVRT mode. Unfortunately, it will make VSC frequently enter and exit LVRT mode. In order to address the tricky problem of transient instability of VSC riding‐through severe grid voltage sag under the weak grid, in this paper, the full‐order large‐signal model of VSC is established first. Then, based on the established model, the analysis indicates VSC will face the risk of losing stability under the weak grid during LVRT due to current transients. By studying the impacts of low short circuit ratio (SCR) on the basin of attraction of the post‐sag equilibrium point, the intrinsic mechanism of VSC losing stability under weak grid conditions is revealed. Moreover, considering that the transient stability is related to the current control, the stability‐enhanced LVRT control ensuring both small‐signal stability and transient stability of VSC during LVRT is proposed. Finally, the correctness of the above theoretical analysis is verified by real‐time simulation [ABSTRACT FROM AUTHOR]

The construction of a household integrated energy system will reduce greenhouse gas emissions and promote sustainable development. Firstly, a household energy system is proposed, which consists of a photovoltaic, wind turbine, electrolysis cell, hydrogen storage tank, and hydrogen‐fired gas turbine. The proposed system is modelled as a bi‐objective optimization problem in which the minimum daily system economic cost, and the minimum loss of energy supply probability. Secondly, a novel multi‐objective egret swarm optimization algorithm with strong search capability and fast convergence is proposed. Thirdly, a household load corresponding to a typical day in spring is chosen as the study case. The optimization results show that the daily system economic cost with the optimal number of devices is 97.48 RMB, and the loss of energy supply probability is 8.33% at the lowest. Finally, to validate the efficiency of the proposed method, the proposed method is compared with NSGA‐II (a widely used multi‐objective evolutionary algorithm). The comparison indicates that the proposed method has a better diversity due to the random searchability. As a consequence, the proposed method can be used in the optimization capacity design of the integrated energy system. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) roof‐top systems in real‐time environments are highly susceptible to the commonly occurring scenario of partial shading that occurs due to unavoidable conditions such as the shadow of buildings and trees. The occurrence of partial shading creates a serious mismatch among the modules of PV arrays that results in severe unexpected power losses in PV arrays. The conventional and hybrid configurations used for loss mitigation in PV arrays are sometimes unreliable and incapable of generating higher power during partial shading. Hence, this paper focuses on the development of a simple and effective technique for the reliable operation of PV arrays and loss reduction during the occurrence of partial shading. The technique is applicable to arbitrary array sizes having no dynamic parts and requires no switches and sensors for implementation to enhance the performance and operation of the system. The performance and reliability of the technique have been investigated under MATLAB simulation and real‐time scenarios by comparing it with various configurations under shading. The investigation reveals a higher power output by the proposed technique with an average conversion efficiency of higher than 90% and power enhancement of 19.07% than other configurations. [ABSTRACT FROM AUTHOR]

As the penetration of distributed photovoltaic (PV) in the distribution network continues to increase, the problem of voltage violation is becoming increasingly prominent. PV output has strong volatility and uncertainty, leading to higher requirements on speed and accuracy of voltage control. The voltage qualification rate of the distribution network with PV is difficult to guarantee if relying on traditional measuring installation and discrete regulators. Thus, it is necessary to introduce high‐frequency sampling devices and various continuous regulators in real‐time voltage control. In this paper, a fast voltage control strategy by real‐time coordination of dynamic voltage regulators (DVRs) and PV inverters based on phasor measurement units (PMU) and remote terminal unit (RTU) information is proposed. The voltage sensitivity is calculated online based on the fast measurement information of PMU. The equivalent models of rapid voltage regulators including DVR and PV inverter are established for the online extraction of voltage optimal control gradients. According to the model predictive control (MPC) theory, a feeder‐level global voltage control strategy is proposed comprehensively considering the objectives of voltage qualification rate, control cost, and network loss, which can realize real‐time voltage control in the distribution network at the millisecond scale. The effectiveness and rapidity of the proposed global voltage control strategy are verified in the IEEE 33‐bus system and a real representative distribution system for preventing voltage violation. [ABSTRACT FROM AUTHOR]

To solve the problems of voltage violation and fast voltage fluctuation caused by photovoltaic (PV) grid connections in distribution networks, a two‐stage coordinated voltage control strategy for PV inverters with multimode coordinated operation in distribution networks (DNs) is proposed. It is based on the characteristics of PV inverters that can be regulated rapidly and continuously. First, the voltage control process is divided into two control stages according to the voltage change rate, and the corresponding voltage control models are established. Then, the operating modes of PV inverters are classified into three modes: economic prevention, economic control, and efficient control. Different operating modes are used to adapt to the two‐stage voltage control. Finally, a voltage fast control solution algorithm is proposed, which considers adaptive time delays and combines local measurement, prediction, and communication information to update control commands. The effectiveness and feasibility of the proposed voltage control strategy were demonstrated through simulation tests on a real 10‐kV line with a high PV penetration rate in the Anhui province of China. The results show that the proposed strategy can prevent voltage violation and sudden changes and can smooth out voltage fluctuation. [ABSTRACT FROM AUTHOR]

In order to ensure the safe and reliable operation of photovoltaic‐storage DC microgrid in islanded mode, this paper proposes a piecewise coordinated control strategy based on fuzzy logic to independently judge the working mode of microgrid. Firstly, according to the operation characteristics of microgrid, the device‐level control of each unit (photovoltaic, energy storage and load) is designed successively. Then, according to the requirement of supply and demand balance of microgrid, fuzzy logic is introduced to realize the independent division of threshold nodes on the basis of piecewise control, so as to ensure the fast switching of working mode and stable operation of the system in various complex environments, that is system‐level control. And the method of impedance analysis is used to analyze the stability of microgrid under different working modes. Finally, the effectiveness and feasibility of the proposed coordination control strategy are verified by MATLAB/Simulink simulation results. [ABSTRACT FROM AUTHOR]

In this research work, an adaptive TS‐fuzzy based RBF neural network (ATSFRBFNN) algorithm based maximum power point tracker (MPPT) is employed for grid integrated photovoltaic (PV) system. The novel learning algorithm provides accurate and rapid PV power tracking under fluctuating solar insolation. However, an improved damping circulating current limiting inverter controller is employed to generate gating signal of voltage source inverter and to deliver reduced harmonic constant, less power loss, mitigation of power quality issues regulation of Dc‐link voltage utilization for grid integrated PV system under steady, and transient weather conditions. The experimental results justify the improved system performance under varying operating conditions. The behaviour of novel hybrid PV MPPT structure is compared with TS‐fuzzy, RBFNN, and GA‐RBF for higher tracking efficacy, least tracking deviation, accurate responses, and zero steady‐state power oscillation around MPP region. [ABSTRACT FROM AUTHOR]

Partial shading conditions (PSC) across the solar photovoltaic (PV) array reduce the overall power yield of the PV plant. Therefore, it is essential to decrease mismatch power loss (MPL) incurred under PSC. For this purpose, total‐cross‐tied (TCT)‐based 'hybrid interconnection' schemes of solar PV array are investigated in the present research work. Proposed novel hybrid bridge‐link (BL)‐TCT and honey‐comb (HC)‐TCT interconnection schemes are found to be effective alternatives to achieve increased solar PV array output power. The performance of the proposed 'hybrid interconnection' schemes is studied by subjecting the 9 × 9 sized solar PV array under four different categories of PSC. The efficacy of the proposed 'hybrid interconnection' schemes is justified to be superior by comparing the simulation results obtained from the proposed interconnection schemes with the earlier reported conventional 'S‐P, B‐L, H‐C, and traditional TCT' interconnection schemes. Further, it is observed that output power is additionally enhanced using a bypass diode connected across individual modules of the solar PV array. The simulation results indicate that the proposed 'hybrid interconnection' schemes with values of 17.5%, 21.28%, 54.33%, and 50.4% under four categories of typical PSC have the lowest MPL relatively compared to that of literature‐reported conventional interconnection schemes. In addition, the proposed 'hybrid interconnection' schemes with the performance enhancement (PE) values of 82.5%, 78.7%, 45.66%, and 49.6% under four categories of typical PSC have the highest PE relatively compared to literature‐reported conventional interconnection schemes. Based on the comparative analysis of results obtained upon four typical PSC, it is concluded that the proposed 'hybrid interconnection' schemes provide a relatively superior output performance compared to that of the conventional 'S‐P, B‐L, H‐C, and traditional TCT' interconnection schemes. [ABSTRACT FROM AUTHOR]

Parameter estimation for solar photovoltaic (PV) models is a challenging issue due to the complex nonlinear multivariable of the current‐voltage and power‐voltage characteristics. In this article, an improved heap‐based algorithm (IHBA) is proposed to improve the performance of a recently published algorithm called heap‐based algorithm (HBA). The HBA's performance is enhanced by applying an effective exploitation feature to boost the searching around the leader position with the goal of enhancing its global search capabilities and avoiding becoming trapped in a local optimum. The proposed IHBA and the standard HBA are developed considering the practical limits of the electrical parameters of PV models to minimize the root mean square error (RMSE) between the experimental and simulated results. The numerical analyses for the PVM‐752GaAs PV module including single‐diode model (SDM), double‐diode model (DDM) and triple‐diode model (TDM) are investigated to estimate five, seven, and nine electrical parameters of these models, respectively. Besides, different recent optimization techniques are simulated with fair comparisons, which are forensic‐based investigation (FBI), equilibrium optimizer (EO), Jellyfish Search (JFS), HBA, Marine Predator Algorithm (MPA) and Enhanced MPA (EMPA), and are compared with the proposed IHBA. Several separate runs are illustrated for the proposed IHBA in comparison with others, whereas the whisker box plot and T‐tests are activated to evaluate their effectiveness metrics. The simulation results derive higher superiority of the proposed IHBA with the minimum RMSE objective and standard deviation of (0.000228 and 3.7 × 106), (0.000184 and 1.93 × 105) and (0.000017 and 2.11 × 105) for SDM, DDM, and TDM, respectively, with respect to the standard HBA, other recent and reported optimization techniques. Additionally, the P‐indicator is applied in this study to illustrate the evidence for the alternative hypothesis. Small values of the P‐indicator for the proposed IHBA are obtained compared to the standard HBA and other recent optimization techniques, where IHBA achieves p‐value of 1.0336 × 1017, 4.037 7× 1011 and 2.1490 × 1010 for SDM, DDM and TDM, respectively. The H‐value is always one which elucidates that the hypothesis probability is not more than 5% for all algorithms for SDM, DDM and TDM. Moreover, the proposed IHBA demonstrates higher efficiency as it provides the first ranks in the confidence interval values compared with other algorithms. Furthermore, the proposed IHBA is significantly employed for the SQ_150 PV module considering the TDM with diverse solar irradiance and temperatures, where significant closeness between the emulated and experimental P–V curves is evidently demonstrated. [ABSTRACT FROM AUTHOR]

This paper presents a state‐of‐the‐art comprehensive review of the modern control techniques for the widely recommended two distributed generation systems (DGSs) using permanent magnet synchronous generator (PMSG)‐ and doubly‐fed induction generator (DFIG)‐based wind turbines (WTs) and photovoltaic (PV) arrays in grid‐connected applications. The already published review papers individually study each DGS and also lack a systematic discussion on the control techniques without any suitable classification. This makes it difficult to study the comparatively improved performance of each control technique and comprehensively understand the control requirements of overall DGSs. Unlike existing literature, this paper first distinctively presents the generalized control configurations for both DGSs in the form of control blocks (CBs). Second, the modern control techniques suggested for each DGS are uniquely classified based on the CBs. Third, each CB is studied in detail considering its required control operation for the particular DGS component and recently proposed control techniques for its implementation. Furthermore, a detailed data analysis and comparative graphical illustrations are provided to appropriately study the merits and demerits of every state‐of‐the‐art control technique. Finally, the useful future research directions are presented to realize improved hardware components and control techniques for the grid‐connected WTs and PV arrays DGSs. [ABSTRACT FROM AUTHOR]

Realising an accurate estimation of model parameters for solar cells and the Photovoltaic modules has serious importance for enhancing the performance of their control systems. Three neoteric metaheuristic methods of Artificial Ecosystem‐based optimisation, Coot Bird‐based optimisation, and Equilibrium optimiser have been applied and evaluated concerning the accurate estimation of various Photovoltaic models. The validation of the applied methods has occurred for valuing the model parameters of R.T.C. France solar cell, and Thin‐film ST40 Photovoltaic module. The objective function has been formulated as the Root Mean Square Error between the actual and estimated data. Matlab/Simulink has been used for the verification of the optimisation methods. The outcomes demonstrate that: (1) The three optimisation algorithms can resolve the problem of the Photovoltaic parameter estimation; (2) There are small distinctions between the three algorithms concerning their best value of the impartial function; this distinction between the best and worst algorithm is 10–9 for R.T.C. France solar cell for SDM; (3) The best algorithm considering the best value of the objective function is Artificial Ecosystem‐based optimisation for R.T.C. France solar cell; (4) Statistical results prove that the three algorithms have tracking efficiencies of 100%, 99.999%, and 98.285% for Artificial Ecosystem‐based optimisation, Coot Bird‐based optimisation, and Equilibrium optimiser, respectively, based on 10 individual runs for R.T.C. France solar cell for SDM. Moreover, the simulation results show that the I/V curves obtained employing Artificial Ecosystem‐based optimisation, Coot Bird‐based optimisation, and Equilibrium optimiser techniques were also matched with the corresponding datasheet curves with the Artificial Ecosystem‐based optimisation and Coot Bird‐based optimisation's predominance in standings the convergence speed, tracking efficiency, statistical indices, and solution accuracy. [ABSTRACT FROM AUTHOR]

Virtual synchronous generator (VSG) control for increasing inverter‐based sources is characterized by attractive features such as supporting virtual inertia, flexible design, and enhancing frequency stability. However, the performance and status of each VSG unit lead to differences in the inertial support capabilities. This paper investigates a cooperative adaptive inertial control method for multiple photovoltaic and energy storage units (PV‐ESUs) to improve system inertia distribution capability during transient events. The frequency discrimination of power disturbances is realized through filters, and the operating ranges of different types of energy storage devices are determined. For the coordination problem with each VSG unit under low‐frequency disturbance. The coordination controller, which regulates virtual inertia values by using technique for order preference by similarity to ideal solution (TOPSIS) evaluation algorithm, is proposed to adaptively adjust the inertial output capability of each VSG unit and provide optimized dynamic frequency support to the grid. The state of charge (SOC) of energy storage, converter adjustable capacity, the maximum adjustable output power of the system per unit time, and other influence factors are considered as actual multiple operation constraints. Finally, the effectiveness and engineering application value of the proposed control strategy is verified by the hardware‐in‐the‐loop (HIL) experimental test platform. [ABSTRACT FROM AUTHOR]

This paper discusses the umbrella optimizing technique (UOT), a novel approach to track maximum power point (MPP) of a solar photovoltaic (PV) system under partial shading conditions (PSCs).Since PSCs have a direct impact on the power generated by PV modules, many researchers have developed a variety of maximum power point tracking (MPPT) approaches to address this issue. Perturb and Observe (P&O) is a traditional method for tracking the maximum power of a PV module under PSCs. The fundamental issue with this traditional technique is that the algorithm misleads the step size and chases the wrong direction under PSCs to track MPP. The proposed algorithm finds the maximum power peak among several observed peaks caused by partial shading of a PV module. At each instance, the proposed algorithm checks for local or global peaks and adjusts the duty cycle of the inverse SEPIC DC–DC converter accordingly.In experimental study, UOT shows better performance in terms of tracking efficiency, tracking time, output power and output current.It exhibits a power boost of 3.39–4.14% with 9.09–16.66% faster tracking in different PSCs. A comparison of different optimization techniques illustrates that the UOT has higher tracking performance. [ABSTRACT FROM AUTHOR]

The increased energy demand with recent advancement in technology has led the global energy producers to move towards solar energy. Partial shading is a phenomenon that can severely affect the production of the solar power in the photovoltaic system. With partial shading situation, the cascaded solar panels show numerous dummy peaks both in power–voltage (P–V) and current–voltage (I–V) curves, resulting in difficulty of extracting maximum power. A dummy peak elimination based MPPT technique is introduced in proposed work for accurate tracking of maximum power. The proposed dummy peak elimination based MPPT estimates the dummy peaks without imposing any high computational burden for the processor. The proposed technique has better dynamic response and is more compatible for real time application compared to existing optimization‐based techniques. The proposed dummy peak elimination based MPPT is tested on a practical system with 3S configuration having three 249 W PV panels for different shading patterns, which displays acceptable outputs with good dynamic performance and better efficiency. [ABSTRACT FROM AUTHOR]

This paper presents a non‐linear controller based in synergetic controller (SC) that used for Wind and PV systems to reduce the chattering phenomenon, tracks rapidly and maintain the Maximum Power Point (MPP) under different environmental and load conditions. To approve the effectiveness of the proposed approach, comparative analysis of MPPT controller built using Perturb and observation for PV system and torque vector for Wind power system, with MPPT controller implemented using synergetic control in the both renewable sources in the hybrid system. The results show that SC MPPT is robust and maintain the power balancing with meeting multiple objectives of standalone hybrid systems such as rapidity of response, robustness under the external variations and no chattering problem as compared with the other controllers. [ABSTRACT FROM AUTHOR]

A novel nonlinear maximum power point tracking (MPPT) controller for autonomous photovoltaic systems based on synergetic control theory is presented in this paper as a solution to eliminate the chattering drawback provided by sliding mode controller, where the proposed strategy allows to generate continuous control law instead of a switching term. A DC/DC boost converter is introduced in the content of this work as an interface between a photovoltaic array and a resistive load. The developed MPPT controller was tested both in simulations using Matlab/Simulink tool and experimentally using dSPACE RTI 1104 real‐time platform and compared with the sliding mode‐based MPPT controller. Furthermore, the EN 50530 standard test with different ramp gradients values is used to calculate the MPPT efficiency under irradiance changes from the slow to the very fast. Illustrative results that prove the effectiveness and the robustness of the proposed MPPT controller even the non‐uniform conditions (temperatures and solar irradiances) are presented here. The fast response and the accurate tracking to the maximum power point (MPP) with considerable reduction in the oscillations are successfully reached with the developed controller which is much better than sliding mode controller. [ABSTRACT FROM AUTHOR]

The requirement of high power (MW‐size) power conditioning unit (PCU) has increased to minimise the plant balance of system cost due to a continuous increase in solar photovoltaic (PV) plant capacity. Traditionally used high‐frequency pulse‐width modulated two/three‐level PCUs have high device switching losses and lower conversion efficiency and are not suitable for high power PCU application as high switching losses lead to high heat generation within the converter, which is very difficult to manage, and lower conversion efficiency leads to less revenue realisation. In the present work, fundamental switching selective harmonic elimination (SHE) modulated 11‐level cascaded H‐bridge voltage source converter is utilised for high power MW‐size PCU application, as it has lower switching losses, higher conversion efficiency, and higher AC/DC voltage ratio. The PV plant configuration for a 40 MW (AC) capacity is developed in the MATLAB/Simulink environment and implemented in real‐time simulator OPAL‐RT to validate the design and control. Harmonics, steady‐state, and dynamic performances are demonstrated at constant and changing solar irradiance levels. The converter conduction and switching losses and conversion efficiency at different loading conditions are presented. The converter control with the capacitor voltage balancing method for fundamental switched SHE is presented in detail. [ABSTRACT FROM AUTHOR]

In this study, a fuzzy current‐sensor less maximum power point tracking (MPPT) algorithm is presented. The algorithm proposed to improve the performance and reduce the cost of photovoltaic systems. The proposed algorithm creates the variable step‐size/variable frequency specification in the drift‐free single input voltage sensor (SIVS) method designing a reliable and widely used fuzzy control algorithm. The proposed algorithm will improve the SIVS methods employing the advantages of the fuzzy controllers in the MPPT algorithms based on the real and false error areas analysis concept developed by Tousi et al. (2016). The proposed method will detect exact drift conditions and move to the optimal setpoint at the maximum speed. The method selects the correct direction, appropriate step‐size, and suitable control frequency to increase dynamic efficiency against the drift condition. Also, the steady‐state efficiency of the algorithm will increase because of high‐frequency and low‐power oscillation performances. The proposed method will be simulated and compared in Matlab/Simulink environment and tested in laboratory conditions on the designed solar charge controller. [ABSTRACT FROM AUTHOR]

Shade phenomena affect the PV module's power output, introduce multiple peak points in PV characteristics, and shorten the expected life cycle of the module. In order to prevent this issue, PV modules should reconfigure, namely, altering the electrical connections between PV modules based on the irradiance without changing their physical locations. As a result, the shading effects can distribute over the array and increase power output. This paper proposed a novel reconfiguration technique based on the simulated annealing algorithm to distribute shading impacts on 9×9 total‐cross‐tied (TCT) PV array. In this work, the proposed algorithm produces a reconfigured connection matrix for 9×9 TCT array that can minimize the current differences in each row and improve the power output. Various shading conditions have been taken to investigate the proposed technique by obtained global maximum power point (GMPP), current at GMPP, power loss, fill‐factor, and efficiency. Further, the performance of the proposed technique is validated with other existing reconfiguration methods under partial shadings. In comparison with existing methods, the result shows that the proposed simulated annealing enhanced the power generation of the TCT array under all shading patterns. [ABSTRACT FROM AUTHOR]

This review covers global maximum power point tracking (GMPPT) methods for photovoltaic (PV) systems under partial shading conditions. Unlike the previous review works that primarily focused on soft computing and hybrid GMPPT, this study gives exclusive attention to the improvement achieved by the conventional MPPT (perturb and observe, hill climbing, and incremental conductance). The improved methods include the popular 0.8 × Voc model and, more recently, the skipping algorithms. In addition to providing qualitative descriptions of the available techniques, this work also attempts to provide a fair evaluation of GMPPT to determine their comparative performances. The competing algorithms, which are selected to represent every category (conventional and soft computing and hybrid MPPT), are benchmarked under carefully selected operating conditions and shading scenarios. The evaluation is focused on four main criteria: tracking accuracy, convergence time, length of voltage fluctuations, and transient efficiency during the search for the global maximum power point. The results obtained from this study can become a basis for researchers and designers to select the best MPPT technique for their respective applications. [ABSTRACT FROM AUTHOR]

The requirement of high power (MW-size) power conditioning unit (PCU) has increased to minimise the plant balance of system cost due to a continuous increase in solar photovoltaic (PV) plant capacity. Traditionally used high-frequency pulse-width modulated two/three-level PCUs have high device switching losses and lower conversion efficiency and are not suitable for high power PCU application as high switching losses lead to high heat generation within the converter, which is very difficult to manage, and lower conversion efficiency leads to less revenue realisation. In the present work, fundamental switching selective harmonic elimination (SHE) modulated 11-level cascaded H-bridge voltage source converter is utilised for high power MW-size PCU application, as it has lower switching losses, higher conversion efficiency, and higher AC/DC voltage ratio. The PV plant configuration for a 40 MW (AC) capacity is developed in the MATLAB/Simulink environment and implemented in real-time simulator OPAL-RT to validate the design and control. Harmonics, steady-state, and dynamic performances are demonstrated at constant and changing solar irradiance levels. The converter conduction and switching losses and conversion efficiency at different loading conditions are presented. The converter control with the capacitor voltage balancing method for fundamental switched SHE is presented in detail. [ABSTRACT FROM AUTHOR]

In this study, a fuzzy current-sensor less maximum power point tracking (MPPT) algorithm is presented. The algorithm proposed to improve the performance and reduce the cost of photovoltaic systems. The proposed algorithm creates the variable step-size/variable frequency specification in the drift-free single input voltage sensor (SIVS) method designing a reliable and widely used fuzzy control algorithm. The proposed algorithm will improve the SIVS methods employing the advantages of the fuzzy controllers in the MPPT algorithms based on the real and false error areas analysis concept developed by Tousi et al. (2016). The proposed method will detect exact drift conditions and move to the optimal setpoint at the maximum speed. The method selects the correct direction, appropriate step-size, and suitable control frequency to increase dynamic efficiency against the drift condition. Also, the steady-state efficiency of the algorithm will increase because of high-frequency and low-power oscillation performances. The proposed method will be simulated and compared in Matlab/Simulink environment and tested in laboratory conditions on the designed solar charge controller. [ABSTRACT FROM AUTHOR]

Shade phenomena affect the PV module's power output, introduce multiple peak points in PV characteristics, and shorten the expected life cycle of the module. In order to prevent this issue, PV modules should reconfigure, namely, altering the electrical connections between PV modules based on the irradiance without changing their physical locations. As a result, the shading effects can distribute over the array and increase power output. This paper proposed a novel reconfiguration technique based on the simulated annealing algorithm to distribute shading impacts on 9 $\times $× 9 total-cross-tied (TCT) PV array. In this work, the proposed algorithm produces a reconfigured connection matrix for 9 $\times $× 9 TCT array that can minimize the current differences in each row and improve the power output. Various shading conditions have been taken to investigate the proposed technique by obtained global maximum power point (GMPP), current at GMPP, power loss, fill-factor, and efficiency. Further, the performance of the proposed technique is validated with other existing reconfiguration methods under partial shadings. In comparison with existing methods, the result shows that the proposed simulated annealing enhanced the power generation of the TCT array under all shading patterns. [ABSTRACT FROM AUTHOR]

This review covers global maximum power point tracking (GMPPT) methods for photovoltaic (PV) systems under partial shading conditions. Unlike the previous review works that primarily focused on soft computing and hybrid GMPPT, this study gives exclusive attention to the improvement achieved by the conventional MPPT (perturb and observe, hill climbing, and incremental conductance). The improved methods include the popular 0.8 × Voc model and, more recently, the skipping algorithms. In addition to providing qualitative descriptions of the available techniques, this work also attempts to provide a fair evaluation of GMPPT to determine their comparative performances. The competing algorithms, which are selected to represent every category (conventional and soft computing and hybrid MPPT), are benchmarked under carefully selected operating conditions and shading scenarios. The evaluation is focused on four main criteria: tracking accuracy, convergence time, length of voltage fluctuations, and transient efficiency during the search for the global maximum power point. The results obtained from this study can become a basis for researchers and designers to select the best MPPT technique for their respective applications. [ABSTRACT FROM AUTHOR]

This work deals with the implementation, control and operation of a reconfigurable small hydro‐solar photovoltaic (PV)‐battery energy storage (BES)‐based distributed generation system (DGS) working in isolated mode (IM) and grid connected mode (GCM). The DGS consists of a hydro generation‐based on a PMSG integrated with a PV array supported by the BES. It operates in an IM during the grid outage and when the grid is available, it synchronizes and operates in GCM. A frequency adaptive comb filter‐frequency locked loop (FLL)‐based control is adopted to estimate errorless phase angles of the hydro generator and grid voltages for quick and accurate grid synchronization. A third‐order band pass filter‐based control technique is used to extract fundamental component of load currents and determine switching pulses to VSC. The overall multi‐objective control is used to maintain voltage and frequency of the system at the point of common coupling during IM and GCM. The power quality of grid and generator currents are maintained at non‐linear loads. The modified perturb and observe‐based control technique is used to extract maximum power from the PV array and it provides drift‐free operation during the change in insolation with de‐rating capability to protect the BES from overcharging in IM. [ABSTRACT FROM AUTHOR]

Partial shading in photovoltaic (PV) power generating systems is practically unavoidable and is a menace to the PV system. Non‐uniform shading can be caused due to clouds, tree branches, buildings or other nearby items. The non‐linear output characteristics of a PV source varies with temperature and solar insolation. During partial shading condition, the conventional hill‐climbing methods of maximum power point tracking (MPPT) prove to be inefficient. In partial shading, the power–voltage curve exhibits multiple peaks. Conventional MPPT methods get stuck on one of the local maxima and fail to attain the global maximum power point (GMPP). Particle swarm optimisation (PSO) has proved to be a very accurate and powerful technique to find GMPP. In this study, the authors improved the conventional PSO by reducing swarm size, as well as, the number of iterations to achieve MPP at a fast pace with remarkable accuracy, thus making the system more efficient with less computational effort. Simulation and hardware implementation for performance evaluation of the proposed technique is carried out to verify various qualities under different partial shading conditions, such as flexibility, reliability, system‐independence and high accuracy in tracking the GMPP under non‐uniform insolations. [ABSTRACT FROM AUTHOR]

Based on the two‐stage‐type configuration of direct current (DC) photovoltaic (PV) power collection network, the key issues of coordination start‐up and operation mode switching for the converters in different stages are investigated thoroughly in this study. With the assistance of start‐up resistors on low‐voltage DC (LVDC) side, the asynchronous start‐up, master–slave synchronous start‐up and droop synchronous start‐up for the first‐stage maximum power point tracking (MPPT) converters are proposed along with the corresponding inter‐stage coordination scheme. Besides, two start‐up strategies for the second‐stage DC step‐up converter are proposed with the considerations of different scenarios. According to the proposed scenario‐driven coordination strategies, the start‐up of DC collection network could be realised without relying on real‐time communication. The principles, logical flow and parameter design methods of the proposed scenario‐driven coordination start‐up strategies are explained in details while a comparative study is performed. The correctness and effectiveness of the proposed strategies have been verified by the system simulation. The work of this study could be beneficial for the future engineering applications of DC PV generation and integration. [ABSTRACT FROM AUTHOR]

Maximum power point tracking (MPPT) controllers are a key element in photovoltaic (PV) conversion systems since they allow extracting the maximum power from PV generators. Metaheuristic algorithms such as the particle swarm optimisation (PSO) are nowadays widely adopted and have shown their superiority to many other techniques. However, conventional PSO (CPSO) algorithms still suffer from the problem of long convergence time when the range of the search area is large. To overcome this issue, this study proposes a fast fuzzy logic PSO (FL‐PSO) based MPPT controller for PV systems. Unlike CPSO algorithm running with constant key parameters (inertia weight and acceleration coefficients), the proposed method includes a fuzzy inference system that dynamically adjusts these parameters. The effectiveness and rapidity of the proposed FL‐PSO algorithm is validated trough numerical simulations and experimental tests. The obtained results show the superiority of the proposal as compared to CPSO, Jaya and hill climbing algorithms even under partial shading conditions and abrupt change of solar irradiation. [ABSTRACT FROM AUTHOR]

Module‐level distributed maximum power point tracking (MPPT) represents an attractive solution for photovoltaic systems installed in dense urban areas, where panels are often subject to different solar irradiance levels. Model‐based MPPT algorithms are particularly suitable for the purpose: they enable good steady‐state accuracy and fast dynamics thanks to an underlying parametric model of the panel. The target of the present study is deeply investigating the estimation of the model parameters, and the collection of the training database, since they heavily affect overall performance. In this work, parameter values result by maximising energy production considering the training database; under some simplifications, it leads to a weighted least squares problem that can be easily solved. One of the main advantages is the robustness in the presence of some identification data that have been collected under partially shadowed conditions. Moreover, the possibility to gather the training database by running a perturb and observe MPPT is investigated and tested for the first time. Energy production is allowed also during this stage, thus opening the way to a periodic update of the parameters to follow degradation and time drift of the module. Experimental results show that performance is virtually the same as that obtained by computing parameters from a large set of volt‐ampere characteristics. [ABSTRACT FROM AUTHOR]

Operating photovoltaic (PV) systems under partial shading conditions results in significant power losses. To mitigate partial shading effects, distributed maximum power point tracking (DMPPT) architectures have been proposed. An emerging DMPPT technique represented by PV module cascaded converters (MCCs) has been widely reported in the literature. In this architecture, a DC converter is allocated for each PV module to process and maximise its power. In this sense, mismatch effects are mitigated between PV modules. While MCC architecture has prominent advantages and value‐added features, its challenges and limitations cannot be ignored. This study presents a comprehensive review of the state of the art of PV MCC architecture to help readers realise the progress of this DMPPT technique. Several points are extensively discussed and analysed including concept realisation and analysis, DC converter topologies and design optimisation, DMPPT performance limitations, DMPPT control, and protection. The main concepts are reemphasised through a set of simulations. Finally, a list of potential research areas in this field is introduced. [ABSTRACT FROM AUTHOR]

A study has been performed to extract the unknown parameters of a photovoltaic (PV) module using mathematical model‐driven analysis. Neglecting the less significant terms, the number of non‐linear exponential terms are reduced to develop a simplified algorithm to estimate five unknown parameters such as diode ideality factor, series resistance, shunt resistance, diode saturation current and photogenerated current of a single diode model PV module accurately with less computational complexities from the manufacturer's datasheet values. Substituting the estimated values of the unknown parameters, the PV module output power is obtained at different irradiance and temperature, which shows close co‐ordination with the datasheet values. To improve the accuracy in the estimation of the PV module output using the proposed model, the nominal operating cell temperature (NOCT) is considered. The results of the proposed model are verified with the experimental output of two (one 315 Wp and another 3 Wp) PV modules, which shows better correlation with the proposed model considering NOCT. The measurement uncertainty to obtain the output power experimentally is not more than 6.88%. [ABSTRACT FROM AUTHOR]

This study proposes a novel frequency regulation strategy based on a power reserve control method. Unlike existing strategies, irradiance information and complex model estimation are not required, except for the maximum power curve. The suboptimal power point at any irradiance and reserve ratio can be automatically converged with the proposed tracking method. Furthermore, an adaptive step-size tracking method is proposed to improve the output power fluctuation around the suboptimal power point. The simulation results with several scenarios are used to verify that the proposed strategy can quickly and robustly track a given power reserve ratio under the irradiance and reserve ratio variation conditions. The proposed strategy can significantly improve the frequency response of power systems with photovoltaic (PV) generation. [ABSTRACT FROM AUTHOR]

Traditionally, hydro turbine governor applications mainly rely on classical proportional–integral–derivative controllers. A classical controller can perform optimally only at the operating point chosen during the controller design. Since hydro power plants are highly non-linear systems alternative control approaches based on adaptive parameters are needed. Historically, due to the limited computation capabilities of microprocessors and programmable logic controllers (PLCs) used in hydro turbine governors, adaptive control schemes were not frequently applied. However, the latest generation of microprocessors and PLCs facilitate the application of adaptive control scheme based on predictive control algorithm for plants with faster dynamic behaviour. In that regard, this study introduces an adaptive controller based on model predictive control (MPC) algorithm developed and applied to a non-linear simulation model of a laboratory hydro power plant. The applied MPC algorithm is based on a linear prediction model whose parameters are identified offline for different operating points across the plant's operating range. The adaptive control scheme updates the prediction model parameters depending on the current operating point. Furthermore, the predictive control algorithm applied in this study is set up as a quadratic programming (QP) optimisation problem that is solved online using a QP solver in a form of Hildreth's algorithm. [ABSTRACT FROM AUTHOR]

In this study, a novel 'Weight of Set Point Similarity (WSPS)' technique is developed for MPPT (maximum power point tracking) for uniformly shaded solar PV (photovoltaic) panel condition. In classical MPPT algorithms like Perturb and Observe, Incremental Conductance, Hill Climbing etc. the main issues are oscillation in a steady-state condition, and longer tracking time during dynamic change condition. The mitigation of these issues is the key contribution of this developed WSPS technique. In the WSPS technique, the comparative study of the weight of four sets of duty cycle decides the steady-state condition, where drastic decrement of the value of step-change mitigates the oscillation issues in a steady-state condition. Moreover, for dynamic condition detection, here an envelope of power is created, which range below the lowest value of oscillating power and above the highest value of oscillating power. The performance of WSPS technique is evaluated on different types of solar irradiance change condition, through simulation as well as experimentation. Moreover, the superiority of the WSPS technique is proved through comparative analysis with Perturb and Observe the MPPT algorithm. [ABSTRACT FROM AUTHOR]

This research work is concerned with maximum power point tracking (MPPT) in photovoltaic (PV) systems under partially shaded conditions (PSCs) through an improved particle swarm optimisation (PSO). The traditional PSO is investigated toward MPPT and the reasons for delayed convergence characteristics are investigated. Then, a modified PSO is proposed, in which population is reduced sequentially by eliminating less‐promising particles. The new concept is applied to a 30 kW PV plant situated in a school building and the results are analysed. The new methodology is also experimentally verified on a 400 W prototype PV system in the laboratory. Enhanced energy harvesting from the existing 30 kW PV plant is highlighted considering realistic shading patterns and full bright conditions of solar insolation. The computed and measured results clearly demonstrate that the new PSO is a promising candidate for MPPT in PV systems under PSC. [ABSTRACT FROM AUTHOR]

This study presents development of a modified leaky least mean square (MLLMS)‐based control strategy for a single‐stage three‐phase grid‐tied photovoltaic (PV) system. In the proposed MLLMS‐based control scheme, the authors employ an incremental conductance maximum power point tracking algorithm for maximum power extraction, MLLMS algorithm for extraction of fundamental active and reactive components of the load current, delivering PV power to the grid, balancing the grid current and compensating harmonics of the connected loads at the point of common coupling. By virtue of a leakage factor and selection of sum of the exponential of the adaptation error in the cost function, the MLLMS algorithm overcomes the problems of drifting, low convergence and oscillations in weights encountered by some popular adaptive algorithms, e.g. least mean square (LMS) and least mean fourth (LMF) algorithms. The proposed control scheme is simulated in MATLAB/Simulink environment under different load and environmental conditions. Subsequently, the proposed control scheme is realised on a prototype grid‐tied PV system developed in the laboratory. From both the simulation and experimental results, it is observed that the proposed MLLMS algorithm outperforms LMS and LMF algorithms in terms of mean square error, oscillation in weights and total harmonic distortions of the grid currents. [ABSTRACT FROM AUTHOR]

This study proposes a novel frequency regulation strategy based on a power reserve control method. Unlike existing strategies, irradiance information and complex model estimation are not required, except for the maximum power curve. The suboptimal power point at any irradiance and reserve ratio can be automatically converged with the proposed tracking method. Furthermore, an adaptive step‐size tracking method is proposed to improve the output power fluctuation around the suboptimal power point. The simulation results with several scenarios are used to verify that the proposed strategy can quickly and robustly track a given power reserve ratio under the irradiance and reserve ratio variation conditions. The proposed strategy can significantly improve the frequency response of power systems with photovoltaic (PV) generation. [ABSTRACT FROM AUTHOR]

Maximum power extraction from the photovoltaic (PV) system plays a critical role in increasing efficiency during partial shading conditions (PSC's). The higher cost and low conversion efficiency of the PV panel necessitate the extraction of the maximum power point (MPP). So, a suitable maximum power point tracking (MPPT) technique to track the MPP is of high need, even under PSC's. This study gives an extensive review of 23 MPPT techniques present in literature along with recent publications on various hardware design methodologies. MPPT classification is done into three categories, i.e. Classical, Intelligent and Optimisation depending on the tracking algorithm utilised. During uniform insolation, classical methods are highly preferred as there is only one peak in the P–V curve. However, under PSC's, the P–V curve exhibits multiple peaks, one global MPP (GMPP) and the remaining are local MPPs. Hence, Intelligent and Optimisation techniques came into limelight to differentiate the GMPP out of all LMPPs. Every MPPT technique has its advantages and limits, but a streamlined MPPT is drafted in numerous parameters like sensors required, hardware implementation, tracking in PSC's, cost, tracking speed and tracking efficiency. This present study aimed to address the advancement in this area for further research. [ABSTRACT FROM AUTHOR]

In this study, a novel 'Weight of Set Point Similarity (WSPS)' technique is developed for MPPT (maximum power point tracking) for uniformly shaded solar PV (photovoltaic) panel condition. In classical MPPT algorithms like Perturb and Observe, Incremental Conductance, Hill Climbing etc. the main issues are oscillation in a steady‐state condition, and longer tracking time during dynamic change condition. The mitigation of these issues is the key contribution of this developed WSPS technique. In the WSPS technique, the comparative study of the weight of four sets of duty cycle decides the steady‐state condition, where drastic decrement of the value of step‐change mitigates the oscillation issues in a steady‐state condition. Moreover, for dynamic condition detection, here an envelope of power is created, which range below the lowest value of oscillating power and above the highest value of oscillating power. The performance of WSPS technique is evaluated on different types of solar irradiance change condition, through simulation as well as experimentation. Moreover, the superiority of the WSPS technique is proved through comparative analysis with Perturb and Observe the MPPT algorithm. [ABSTRACT FROM AUTHOR]