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Recently renewable energy such as a photovoltaic (PV) system has been utilized more and more since it is pollution-free and permanent. To maintain the PV system functioning at, or near, the peak power point of the PV panel under different conditions such as fluctuating solar irradiation, temperature, and other factors, maximum power point tracking algorithms are required. In this study, a novel hybrid robust intelligent controller for a photovoltaic system is proposed. Three loops are used for creating the proposed controller, ensuring the controller's robustness. The first loop's objective is to locate the photovoltaic system's highest power spots. In the second loop, a novel fractional-order sliding model observer based on deep reinforcement learning optimization approach is proposed as a result of the design of a reliable controller under the lumped uncertainty in the system. Designing a novel back-stepping fast non-singular terminal fractional-order sliding mode controller is achieved at the final step. This method offers a nice transition response, a small tracking error, with a quick response to changes in solar radiation. Numerical analysis shows that the performance of the photovoltaic system by the proposed controller has been able to increase the efficiency of the system significantly. [ABSTRACT FROM AUTHOR]

The efficiency of standalone photovoltaic (PV) systems heavily relies on the effectiveness of their maximum power point tracking (MPPT) controller. This study aims to improve the operational efficiency and reliability of standalone PV systems by introducing a novel control scheme, the Immersion and Invariance Neural Network (II-NN). This innovative system integrates a nonlinear estimator of solar irradiance with a neural network (NN) model, eliminating the need for direct irradiance measurements and associated costly sensors. The proposed methodology uses the Immersion and Invariance algorithm to design a nonlinear estimator that leverages the real-time measurements of PV current and voltage to estimate the incident irradiance. The NN then processes this estimated irradiance to determine the MPP voltage accurately. A robust nonlinear controller ensures the PV system operates at the MPP. This approach stands out by managing the nonlinearities, parametric uncertainties, and dynamic variations in PV systems without relying on direct irradiance measurements. The II-NN system was rigorously tested and validated under real climatic conditions, providing a realistic performance assessment. The principal results show that the II-NN system achieves a mean error of 0.0183V and a mean absolute percentage error of 0.3913%, with an overall MPPT efficiency of up to 99.84%. Comparisons with the existing methods, including perturb and observe, incremental conductance, and three other recent algorithms, reveal that the II-NN system outperforms these alternatives. The major conclusion is that the II-NN algorithm significantly enhances the operational efficiency of PV systems while simplifying their implementation, making them more cost-effective and accessible. This study substantially contributes to PV system control by advancing a robust, intelligent, and sensorless MPPT control scheme that maintains high performance even under varying and unpredictable climatic conditions. [ABSTRACT FROM AUTHOR]

The Photovoltaic system (PV) is gaining popularity as a future energy source due to its vast, secure, essentially limitless, and widely accessible nature. However, a prevalent challenge in PV systems is the impact of solar cell radiation intensity and temperature on the output power induced in the photovoltaic module. As a result, monitoring the input source's maximum power point becomes essential for maximizing the effectiveness of the renewable energy system. In order to harvest the most power from the photovoltaic system and channel it to the load through a boost converter, which raises the voltage to the necessary level, most Power Point Tracking, or MPPT, is essential. This study uses the Perturb and Observe (P&O) method to track the photovoltaic module's Maximum Power Point (MPP) and maximize the system's overall power production to overcome this obstacle. By varying the array terminal voltage or current, the P&O approach perturbs the system and then compares the PV output power to the preceding perturbation cycle. Therefore, the main goal of this study is to simulate and assess the effectiveness of the P&O MPPT algorithm in MATLAB/Simulink when it comes to maximizing power extraction from a photovoltaic system. The simulation findings show that the suggested P&O approach is a reliable means of tracking MPP from PV panels, even when there are fluctuations in solar irradiation. [ABSTRACT FROM AUTHOR]

In this article, a model predictive control (MPC) with common-mode voltage (CMV) suppression is proposed for single-phase cascaded H-bridge (CHB) inverters, which can also simultaneously achieve control objectives of gridconnected current tracking, voltages balancing of different H-bridge submodules on the DC-side and switching frequency reduction. To suppress high-frequency components of the common-mode voltage without additional switching devices, the algorithm proposed designs the predicted and reference values of the CMV and incorporates them in the cost function. At the same time, the capacitor voltages balancing control is integrated in the calculation of the optimal modulation function of the H-bridge, which reduces the complexity of control effectively. Besides, switching times of the MOSFETs are compared in two cycles. The cost function is constructed to represent comprehensive effect of the control. Finally, an experiment is performed on the hardware-in-the-loop experimental platform. The experimental results show that the proposed algorithm can offer a better voltage THD and reduce the times of switch action by nearly half while maintaining high-precision current tracking and maximum power point of photovoltaic modules, which alleviate the potential electromagnetic interference and cabling problem. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

This article presents a low‐cost maximum power point tracking (MPPT) charge controller designed to efficiently charge solar batteries using a simple and robust MPPT method, while providing protection against over‐currents and over‐voltages. The proposed MPPT approach utilizes a modified Incremental Conductance (INC) algorithm adapted to low‐cost embedded controllers, as it avoids all the mathematical division calculations typically used in the conventional INC algorithm. It also introduces a modified step size based only on photovoltaic (PV) power change to enhance tracking accuracy and convergence speed. Furthermore, to reduce the stress and switching losses of DC–DC converter, a feedback control with two proportional and integral (PI) compensators is also used to control both the PV voltage and to protect the battery from over‐currents using PV power limitation technique. Simulation results, carried out under MATLAB/Simulink, demonstrate the good performance of the proposed control scheme regarding any variations in atmospheric conditions, both for the MPPT control and for the battery charge control. Finally, a processor‐in‐the‐loop (PIL) test using a low‐cost Arduino MKR ZERO board is also performed to verify the hardware implementation of the proposed method. PIL results are in totally accordance with MATLAB/Simulink simulation results, which further proves the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Photovoltaics are one of the most important renewable energy sources to meet the increasing demand for energy. This led to the emergence of Microgrid s, which revealed a number of problems, the most important of which is managing and monitoring their operation, this research contributes mainly by using a maximum power tracking algorithm Which depends on artificial neurons and integrating it with a proposed algorithm for energy management in Standalone DC Microgrid, in order to control the distribution of power and maintain the DC bus voltage level. Maximum Power Point Tracking (MPPT) algorithm based on ANN+PID is used. Where ANN tracks the maximum power point by estimating the reference voltage using real-time data such as temperature and solar radiation. The PI controller reduces the error between the measured voltage and the reference voltage and makes the necessary adjustments in order to control the boost converter connected to the photovoltaic panels. While the process of controlling the DC bus voltage level is done by controlling the battery charging and discharging process through the power management algorithm and controlling the Bidirectional converter switches according to the battery's state of charge. The simulation results obtained by used MATLAB Simulink are shown that the used MPPT algorithm achieved the maximum power with the least amount of fluctuation, the method's efficiency was 99.92%, and its accuracy was 99.85%, as well as the success of the power management algorithm controlling the battery charging/discharging process and maintaining the DC voltage level at the specified value in different operating scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a novel CMOS RF-to-DC converter for ultra-low-power wireless sensor nodes powered by RF wireless power transfer. The proposed converter achieves 10% higher power conversion efficiency than a conventional rectifier, with only a 1% increase in power consumption. The system employs a reconfigurable Dickson topology, operates on the unlicensed 868 MHz ISM band, and includes a built-in power-efficient MPPT system architecture. Experimental measurements show a maximum power conversion efficiency of 55% in the power range from −22 dBm to 0 dBm, with a power sensitivity of −22 dBm for a DC output voltage of 2.4 V. The proposed converter offers a promising solution for efficient wireless power transfer and energy harvesting in ultra-low-power wireless sensor nodes. [ABSTRACT FROM AUTHOR]

This paper deals with the control of maximum power production in a grid-connected photovoltaic system. The on-grid system ensures the continuity of electricity supply to consumers. It also offers less investment and operation cost due to the removal of battery requirements normally indispensable to guarantee supply continuity. To maintain the maximum output power production of photovoltaic panels regardless of changes in irradiation, proper control of the boost converter is necessary. The known perturb-and-observe method is explored and modified to improve performance. The modification is proposed by adding the measurement of the current variable to reduce the oscillation around the maximum power point once it is achieved. The connection to an existing single-phase alternating-current power grid is accomplished by implementing sinusoidal pulse-width modulation on the inverter. Phase-angle synchronization between the inverter output and the existing power grid is accomplished by using the phase-lock loop technique. Simulation of the proposed method showed that an improvement of the photovoltaic function up to 98% has been achieved. [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]

Renewable energy is promoted massively to overcome problems that fossil fuel power plants generate. One popular renewable energy type that offers easy installation is a photovoltaic (PV) system. However, the energy harvested through a PV system is not optimal because influenced by exposure to solar irradiance in the PV module, which is constantly changing caused by weather. The maximum power point tracking (MPPT) technique was developed to maximize the energy potential harvested from the PV system. This paper presents the MPPT technique, which is operated on a new high‐gain voltage DC/DC converter that has never been tested before for the MPPT technique in PV systems. Fuzzy logic (FL) was used to operate the MPPT technique on the converter. Conventional and adaptive perturb and observe (P&O) techniques based on variables step size were also used to operate the MPPT. The performance generated by the FL algorithm outperformed conventional and variable step‐size P&O. It is evident that the oscillation caused by the FL algorithm is more petite than variables step‐size and conventional P&O. Furthermore, FL's tracking speed algorithm for tracking MPP is twice as fast as conventional P&O. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems utilize maximum power point tracking (MPPT) controllers to optimize power output amidst varying environmental conditions. However, the presence of multiple peaks resulting from partial shading poses a challenge to the tracking operation. Under partial shade conditions, the global maximum power point (GMPP) may be missed by most traditional maximum power point tracker. The flower pollination algorithm (FPA) and particle swarm optimization (PSO) are two examples of metaheuristic techniques that can be used to solve the issue of failing to track the GMPP. This paper discusses and resolves all issues associated with using the standard FPA method as the MPPT for PV systems. The first issue is that the initial values of pollen are determined randomly at first, which can lead to premature convergence. To minimize the convergence time and enhance the possibility of detecting the GMPP, the initial pollen values were modified so that they were near the expected peak positions. Secondly, in the modified FPA, population fitness and switch probability values both influence swapping between two-mode optimization, which may improve the flower pollination algorithm's tracking speed. The performance of the modified flower pollination algorithm (MFPA) is assessed through a comparison with the perturb and observe (P&O) method and the standard FPA method. The simulation results reveal that under different partial shading conditions, the tracking time for MFPA is 0.24, 0.24, 0.22, and 0.23 s, while for FPA, it is 0.4, 0.35, 0.45, and 0.37 s. Additionally, the simulation results demonstrate that MFPA achieves higher MPPT efficiency in the same four partial shading conditions, with values of 99.98%, 99.90%, 99.93%, and 99.26%, compared to FPA with MPPT efficiencies of 99.93%, 99.88%, 99.91%, and 99.18%. Based on the findings from simulations, the proposed method effectively and accurately tracks the GMPP across a diverse set of environmental 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]

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]

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]

Incorporating bypass diodes within photovoltaic arrays serves to mitigate the negative effects of partial shading scenarios. These situations can lead to the appearance of multiple peaks in the performance of solar panels. Nevertheless, there are cases where conventional maximum power point tracking (MPPT) techniques could encounter inaccuracies, causing them to identify the highest power point within a specific area (the local maximum power point; LMPP) instead of the overall highest power point across the entire array (the global maximum power point; GMPP). Numerous methods based on artificial intelligence (AI) were proposed to address this issue; however, they frequently used cumbersome and unreliable methodologies. This research presents the energy-valley-optimizer-based optimization (EVO) technique, which is designed to efficiently and dependably tackle the issue of partial shading (PS) in detecting the maximum power point (MPP) for photovoltaic (PV) systems. The EVO algorithm enhances the speed of tracking and minimizes power output fluctuations during the tracking phase. Through the utilization of the Typhoon hardware-in-the-loop (HIL) 402 emulator, extensive validation of the proposed technique is conducted. The effectiveness of the suggested method is compared with the established cuckoo search algorithm for achieving maximum power point tracking (MPPT) within a photovoltaic (PV) system. This comparison takes place under equivalent conditions to ensure a fair performance evaluation. [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 main element of solar power plants is usually a power cascade (DC/DC converter, inverter). Converters in such generation systems should have high efficiency (at least 90%), high output signal quality and ensure operation of the power plant with maximum selection of power from the solar battery. The characteristics of solar panels depend heavily on weather conditions such as light and temperature. During the day, the temperature and power of the solar generator are constantly changing. These changes result in a shift in the maximum power point and a partial loss of power. In order to obtain the maximum possible power from the solar battery, it is necessary to use the appropriate maximum power point tracking algorithm (MPPT). For MPPT, specialized controllers are used, which use one of the algorithms to optimize the working point of the photomodules. The most commonly used methods are perturbation and observation, increasing conductivity, constant voltage. The maximum power point tracking method used will largely determine the efficiency of the photovoltaic generation system. Maximum power recovery from solar panels is possible only when the battery voltage is continuously regulated at an optimal operating point. Thus, the design and development of modern efficient photovoltaic generation systems should address not only the improvement of high efficiency solar cell technology, but also a number of issues of designing photovoltaic converters and their control systems to significantly improve their energy efficiency. [ABSTRACT FROM AUTHOR]

The major use of a power point tracking controller is to maximize or enhance the power generation in photovoltaic systems. These systems are steered to operate and maximize the power point. Under partial shading conditions, the power points may vary or fluctuate between global maxima and local maxima. This fluctuation leads to a decrease in energy or energy loss. Hence, to address the fluctuation issue and its variations, a new hybridized maximum power point tracking technique based on an opposition-based reinforcement learning approach with a butterfly optimization algorithm has been proposed. The proposed methodology has been tested on 6S, 3S2P and 2S3P photo-voltaic configurations under different shading conditions. Performance comparison and analysis have been presented with a butterfly optimization algorithm, grey wolf optimization algorithm, whale optimization algorithm, and particle swarm optimization-based maximum power point tracking techniques. Experimental results show that the proposed method performs better adaptation than the conventional approaches and mitigates the load variation convergence and frequent exploration and exploitation patterns. [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]

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]

The main role of maximum power point tracker (MPPT) is to adapt the optimal resistance RMPP, corresponding to the maximum power point (MPP) of the photovoltaic generator (GPV), to the impedance of the load for maximum power transfer. This is accomplished through the tuning of the duty cycle D to an optimum value DMPP, that controls a DC-DC converter applied between the GPV and the load Rload. This paper proposes a system that is applicable to any load and enables rapid and precise tracking under variable weather circumstances. The suggested scheme allows simple and direct computation of the control signal DMPP from the values of Rload and RMPP. Rload is computed using two voltage and current sensors, while RMPP is estimated using an artificial neural network (ANN) that employs the solar irradiance, temperature and the GPV internal current-voltage characteristics. Using MATLAB environment, the obtained simulation results reveal better and more effective tracking with nearly no oscillations compared to a relevant ANN-based technique, under various meteorological conditions. [ABSTRACT FROM AUTHOR]

One of the most important areas in today's world is meeting the energy needs of various resources provided by nature. The advantages of renewable energy sources for many application sectors have attracted a lot of attention. The majority of grid-based enterprises use solar photovoltaic (PV) systems to collect sunlight as a reliable energy source. Due to solar PV's simple accessibility and efficient panel design, it is widely used in a variety of application scenarios. By employing the Maximum Power Point Tracking (MPPT) technique, the PV modules can typically operate at their best rate and draw the most power possible from the solar system. Some hybrid control mechanisms are utilized in solar PV systems in traditional works, which has limitations on the problems of increased time consumption, decreased efficiency, and increased THD. Thus, a new Mine Blast Optimization Algorithm (MBOA) based MPPT controlling model is developed to maximize the electrical energy produced by the PV panels under a different climatic situations. Also, an interleaved Luo DC-DC converter is used to significantly improve the output voltage of a PV system with a lower switching frequency. A sophisticated converter and regulating models are being created to effectively meet the energy demand of grid systems. The voltage source inverter is used to lower the level of harmonics and ensure the grid systems' power quality. Various performance indicators are applied to assess the simulation and comparative results of the proposed MBOA-MPPT controlling technique integrated with an interleaved Luo converter. [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]

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]

The fractional open-circuit voltage (FOCV) method is extensively used in low-power energy harvesting (EH) sources to extract maximum power. For fast-varying EH sources, a fast sampling rate is required. This work theoretically analyzes the influence of the sampling time and period on the harvested power of sinusoidal EH sources. In addition, the circuit limitations to achieve a fast sampling rate are presented and circuits to deal with them proposed and implemented. Furthermore, one of the circuits is based on a novel pseudoFOCV method and achieves the fastest sampling rate. Experimental tests are performed with a 2 Hz, 1 to 3 V sinusoidal source having an output resistance of 127 Ω, and the results are shown to agree with theoretical predictions. It is shown that the harvested power increases with the sampling rate when the sampling time is negligible (sampling 15 times faster than the source frequency extracts around 99% of the maximum), and for fixed sampling times, there is an optimum sampling rate where the harvested power is maximum. The first result is generic and valid for methods other than the FOCV. Tests were also performed with a small-scale wave energy converter placed in a linear shaker emulating a sea environment. Harvested power increases by 25% with respect using a commercial FOCV unit with a low sampling rate. [ABSTRACT FROM AUTHOR]

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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]

In this paper, we focused on modeling and simulation of a hybrid solar-wind energy system, consisting of a photovoltaic cell and a wind turbine driven by a Permanent Magnet Synchronous Generator (PMSG). The proposed system gives details of the hybrid solar-wind system. In the PV subsystem, there will be a photovoltaic energy subsystem, MPPT controller, and a DC-DC boost converter. The wind turbine subsystem includes a wind turbine energy subsystem, PMSG, and MPPT controller. The MPPT controllers were designed to extract the maximum power regardless of the weather conditions and temperatures. The P and O algorithm is introduced in the MPPT used in the PV energy subsystem and on the control of the machine side converter (MSC). The proposed wind turbine energy subsystem is based on Direct Torque and Flux Control (DTFC). The simulated results are demonstrated to show that the hybrid solar-wind generating system can realize a maximum power point tracking control of wind and solar power to satisfy the energy demand over an extensive range under unpredictable atmospheric conditions. The system has been demonstrated by MATLAB/Simulink and tested for variable weather conditions and temperatures. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Hybrid stand-alone Small Wind Solar Energy System offers a feasible solution in remote areas where grid connectivity is either financially or physically unavailable. A small wind turbine (SWT) and a solar photovoltaic system are part of the hybrid energy system, which is effectively employed to meet the energy needs of rural household loads. OBJECTIVE: This research suggests an effective analysis of wind solar hybrid system controllers taking energy demands into account. The controller should be designed in such a way as to intelligently monitor the availability of wind energy and solar energy and store the energy without spilling it out. METHODS: In order to cope with the challenging factors involved in designing the controller, intelligent power tracking with an artificially intelligent neural network (AI-NN) is designed. Added to that, the whole process has been designed and analysed with the MATLAB SIMULINK tool. RESUSTS: The results of the simulation, infer that AI-NN achieved the regression value of 0.99 when compared with the Perturb & Observe algorithm (P&O), and the Fuzzy Logic Control (FLC) algorithm, and has a higher tracking speed. Also, the AI-NN attained 2.62kW whereas the P&O has attained 2.52kW and Fuzzy logic has attained 2.43W of power which is 3.89% higher than P&O algorithm and 7.52% higher than fuzzy MPPT algorithm. CONCLUSION: The designed controller module enhances the system by artificially intelligent algorithm. The AI-NN attains the better power performance with lesser tracking time and higher efficiency. Thus, it is evident that AI-NN MPPT suits well for the hybrid system. [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]

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]

This paper presents the modeling of wind energy systems using MATLAB Simulink. The model considers the MPPT (Maximum Power Point Tracking) technique to track the maximum power that could be extracted from the wind energy. The main contribution of our work lines in the choice of the Wind Energy Conversion System (WECS) components and the suitable MPPT technique. An innovating maximum MPPT technique is proposed. This technique combines the advantages of an ameliorated Perturb & Observe (P&O) control algorithm and the speed of Optimal Relationship Based (ORB) control algorithm to allow the MPPT controller output (duty cycle) adjusts the voltage input of a Buck-Boost converter to track the maximum power point. It searches for the system optimum relationship for maximum power point tracking and then controls the system based on this relationship. The main advantage of the proposed technique is that it does not require an anemometer or preknowledge of a system, but has an accurate and fast response to wind speed fluctuations. The MATLAB/Simulink simulation results show that the developed model components choice complies with cost and reliability constraints and confirm the validity and performance of the proposed control algorithm. [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]

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]

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]

Irradiance variability is one of the main challenges for using photovoltaic energy. This variability affects the operation of maximum power point trackers (MPPT) causing energy losses. The logic of the Perturb-and-Observe MPPT algorithm is particularly sensitive to quick irradiance changes. We quantified the existing relation between irradiance variations and efficiency loss of the logic of the Perturb-and-Observe MPPT algorithm, along with the sensitivity of the MPPT to its control parameters. If the algorithm parameters are not tuned properly, its efficiency will drop to nearly 2%. Irradiance variability causes a systematic energy loss of the algorithm that can only be quantified by ignoring the hardware components. With this, we aim to improve the energy yield estimation by providing an additional efficiency loss to be considered in the calculations. [ABSTRACT FROM AUTHOR]

This paper proposes a novel maximum power point tracking (MPPT) method inspired by the horse racing game for standalone photovoltaic (PV) power systems, such that the highest PV power conversion efficiency is obtained. From the horse racing game rules, we develop the horse racing algorithm (HRA) with the qualifying stage and final ranking stage. The MPP can be searched even if there exist multiple local MPPs for the PV power system. Moreover, from the proposed horse racing algorithm, the calculation is reduced, so that the transient searching points are less than traditional methods, i.e., the transient oscillation is less during the MPPT control. Therefore, the HRA based MPPT method avoids local maximum power traps and achieves the MPP quickly even if considering partial shading influence and varying environment for PV panels. Evidence of the accuracy and effectiveness of the proposed HRA method is exhibited by simulation results. These results are also compared with typical particle swarm optimization (PSO) and grey wolf optimization (GWO) methods and shown better convergence time as well as transient oscillation. Within the range from 0.34 to 0.58 s, the proposed method has effectively tracked the global maximum power point, which is from 0.42 to 0.48 s faster than the conventional PSO technique and from 0.36 to 0.74 s faster than the GWO method. Finally, the obtained findings proved the effectiveness and superiority of the proposed HRA technique through experimental results. The fast response in terms of good transient oscillation and global power tracking time of the proposed method are from 0.40 to 1.0 s, while the PSO and GWO methods are from 1.56 to 1.6 s and from 1.9 to 2.2 s, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a new modified buck–boost converter entitled the basic Z‐H8 topology. The modified configuration is composed by adding two dc decoupling and non‐ideal compensation blocks across the dc‐input side of the conventional Z‐H buck converter. These blocks create some benefits such as buck–boost ability and lower stresses and ripples for passive and active elements by up to 50% compared to the traditional Z‐H structure. Moreover, the proposed topology can be used as a dc/dc, dc/ac, or ac/dc converter. Hence, various control techniques based on unipolar PWM and SPWM are proposed for the basic Z‐H8 configuration to act as an inverter. Other valuable advantages are providing galvanic isolation and reducing leakage current by two blocks, which can be extremely useful for photovoltaic (PV) applications. Thus, a grid‐connected single‐stage PV system with a proper model predictive control algorithm is proposed. Despite a continuous voltage on the Z‐H8 inverter output, other benefits of the proposed PV system are tracking the maximum power point, adjusting the injected powers to the grid, and increasing security and reliability. The operating principle of the proposed topologies and control methods are explained in detail with the relevant equations and are confirmed by the simulation and experimental results. [ABSTRACT FROM AUTHOR]

This brief presents an energy harvester IC with the maximum power point tracking (MPPT) scheme for an ultra-wide input power range. In the energy harvester, the proposed auto-configurable dual-mode MPPT algorithm adaptively switches between a fixed charge search (FCS) mode and a fixed frequency search (FFS) mode, according to the input power level. The implemented chip in 180nm CMOS process achieves the widest input power range of 12nW-to-180mW with a peak efficiency of 93.6%, occupying $1.14\,\,{\times }\,\,1.24$ mm2. The proposed hill climbing-based MPPT scheme also shows a conversion efficiency of >30% even at 12nW input power while consuming a quiescent current of 2.2nA. [ABSTRACT FROM AUTHOR]

Maximum power point tracking (MPPT) plays a significant role in a photovoltaic (PV) system's optimal performance. Bypass diodes create multiple peaks on the power–voltage curve during partial shading conditions (PSCs), reducing the PVs overall power extraction capability. This article proposed a new hybridized architecture for switched inductor and switched capacitor circuit based voltage equalizer (VE) topology with reduced switch count to prevent the multiple peaks and increase the energy yield during PSCs. The proposed VE operates with a fixed duty cycle and frequency. The MPPT controller with PSC detection is also proposed to operate VE only when the PSC is detected to avoid unwanted switching and conduction losses. The proposed system's effectiveness for ten modules connected in series has been determined compared with other modular state-of-the-art VEs. The proposed topology can be extended with a smaller number of control switches. On average, the experimental results showed a 30.42% improvement in energy yield in comparison with the conventional systems. Furthermore, the proposed controller offers the MPPT efficiency of 99.28% with a tracking time of 0.10 s for uniform shaded conditions and 0.692 s for PSCs on average. [ABSTRACT FROM AUTHOR]

The integrated circuits employed for power management in photovoltaic (PV) energy harvesting applications are required to perform an efficient maximum power point tracking (MPPT) process for maximizing the power production of the PV source during the continuously changing atmospheric conditions. Among the alternative MPPT methods, the perturbation and observation (P&O) technique has the advantages of operational and implementation simplicity. In this article, a novel PV MPPT control system for on-chip implementation of the P&O MPPT method is presented, which, compared to the past-proposed on-chip MPPT systems, has the advantage that it is implemented based on purely digital CMOS circuits without requiring the use of complex circuits, such as multipliers, sample and hold units, or analog-to-digital converters. Therefore, it can be easily implemented on-chip with low design complexity while simultaneously retaining the high-performance features of the P&O MPPT technique. The proposed PV MPPT system has been fabricated using the XFAB XH018 0.18-μm CMOS technology. The experimental results verify the successful operation of the on-chip PV MPPT control unit and its ability to achieve a high MPPT efficiency over a wide range of operating solar irradiation values. [ABSTRACT FROM AUTHOR]

The laser power transfer system is one of the most promising systems in the long-range wireless power transfer field. However, the low efficiency at photovoltaic (PV) receiver limits the performance of an implemented system due to the Gaussian laser beam. Since the irradiance profile of the Gaussian laser beam is not uniform, the PV array exhibits complex output characteristics with multiple peaks, leading that the conventional maximum power point tracking (MPPT) method is not acceptable either on tracking accuracy or on tracking speed. In this article, a simple and rapid voltage-location global MPPT (VL-GMPPT) method is proposed for PV array under Gaussian laser beam condition (GLBC) in terms of reducing the voltage searching range. In order to do so, the mathematical model of PV array's global maximum power point (GMPP) is developed under GLBC. Then, the VL-GMPPT method incorporates voltage-location mechanism to directly move the operating point to the vicinity of GMPP and employs IncCond method to accurately find the GMPP. The experimental and simulation results are shown to verify the proposed GMPPT method. It was found that the proposed method can reduce 90% of tracking time that is consumed by the conventional global searching method. [ABSTRACT FROM AUTHOR]

This article presents a low-overhead energy harvesting and delivery system (EHDS) with pulse frequency modulated (PFM) integrated voltage regulator (IVR) power conversion and self-tuned maximization of system output power. A novel load-inclusive time-based maximum power point tracking (LI-TB-MPPT) is developed to provide centralized tuning of PFM-IVR operation based on both source capabilities and load demand on-the-fly, and a configurable fractional sample and hold circuit provides adaptive harvesting window control. The proposed EHDS enables robust harvesting while relieving the use of high passives, with over two orders of magnitude reduction, at the cost of only slight decrease in end-to-end efficiency compared to prior works. Furthermore, a low-overhead wake-up assist circuit utilizes cold-configuration of harvesting sources for efficient and accelerated cold-start. The proposed EHDS is demonstrated in a 65 nm CMOS process with commercial photovoltaic energy harvesting modules. Using only 1.2 and 1 $\mu$ H of passives, measured results show a peak 74.9% end-to-end efficiency (simulated up to 85% at 47 $\mu$ H) and a fast startup time of 3.8 ms. Up to 15% increase in conversion efficiency against load and input voltage variations is achieved with LI-TB-MPPT. The results demonstrate a compact solution for self-sustained cost-restricted stand-alone systems. [ABSTRACT FROM AUTHOR]