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In this paper, a hybrid optimization controller that combines a genetic algorithm (GA) and ant colony optimization (ACO) called GA-ACO algorithm is proposed. It is applied to a photovoltaic module array (PVMA) to carry out maximum power point tracking (MPPT). This way, under the condition that the PVMA is partially shaded and that multiple peaks are produced in the power-voltage (P-V) characteristic curve, the system can still operate at the global maximum power point (GMPP). This solves the problem seen in general traditional MPPT controllers where the PVMA works at the local maximum power point (LMPP). The improved MPPT controller that combines GA and ACO uses the slope of the P-V characteristic curve at the PVMA work point to dynamically adjust the iteration parameters of ACO. The simulation results prove that the improved GA-ACO MPPT controller is able to quickly track GMPP when the output P-V characteristic curve of PVMA shows the phenomenon of multiple peaks. Comparing the time required for tracking to MPP with different MPPT approaches for the PVMA under five different shading levels, it was observed that the improved GA-ACO algorithm requires 19.5~35.9% (average 29.2%) fewer iterations to complete tracking than the mentioned GA-ACO algorithm. Compared with the ACO algorithm, it requires 74.9~79.7% (average 78.2%) fewer iterations, and 75.0~92.5% (average 81.0%) fewer than the conventional P&O method. Therefore, it is proved that by selecting properly adjusted values of the Pheromone evaporation rate and the Gaussian standard deviation of the proposed GA-ACO algorithm based on the slope scope of the P-V characteristic curves, a better response performance of MPPT is obtained.

Photovoltaic (PV) cells are very costly because of the silicon element which is not cheaply available. Usually, PV cells are preferred to be used at maximum efficiency. Therefore, PV plants are emphasized to extract maximum power from PVcells. When inertia free PV plants are integrated into the grid in large numbers, the problem of maintaining system stability subjected to load perturbation is quite difficult. In response to this, a control topology is being an approach to make available the PV cells in maintaining system stability by utilizing the system frequency deviation as feedback to the controller. To implement this, the PVs are operated at Maximum Power Point Tracking (MPPT). This allows the PV to operate at Pseudo Maximum Power Point tracking (PMPPT) which makes it possible to run the PV with reserve power capacity without employing a battery for storage. The control strategy has been implemented over a two-stage power conversion model of the PV system. The simulation results showed that the proposed control PMPPT topology is effective in frequency regulation capability as compared to the MPPT technique.

Maximum power point tracking (MPPT) techniques extract the ultimate power from the photovoltaic (PV) source. Therefore, it is a fundamental control algorithm in any PV configuration. The research in this area is rich and many MPPT methods have been presented in the literature. However, in the current study, we focus on the PV model-based MPPT algorithms. In this regard, the classification of this category can be mainly divided into curve fitting methods and techniques based on the mathematical model or characteristics of the PV source. The objective of the PV model-based MPPT algorithm is to allocate the position of the maximum power point (MPP). Thus, no searching efforts are required to capture that point, which makes it simple and easy to implement. Consequently, the aim of this study is to give an overview of the most commonly utilized model-based MPPT methods. Furthermore, discussion and suggestions are also addressed to highlight the gap in this area. The main methods from the literature are compared together. The comparison and evaluation are validated using an experimental hardware-in-the-loop (HIL) system, where high efficiency (more than 99%) can be obtained with a simple calculation procedure and fast convergence speed.

Grid-tied solar photovoltaic systems use a PWM-VSI STATCOM to regulate active and reactive power. Due to high reactive power demand, often it has been experienced that there is a decay in reactive power supply which may cause malfunction in the load side equipments. The STATCOM balances power variations caused by solar irradiation and ensures constant DC bus voltage for efficient power conversion and optimal MPPT performance. It also provides dynamic reactive power support, balancing imbalanced loads and filtering harmonics. The modified PWM-VSI controlled by Genetic Algorithm optimized Fractional Order based STATCOM approach enhances dynamic response, improves system efficiency, and integrates with MPPT (SAR) for simultaneous reactive power compensation and extraction. The proposed system ensures grid stability during variable solar generation and outperforms the P&O MPPT controller in active and reactive power management. The proposed system uses a modified PWM-VSI STATCOM controller (FOSTATCOM) to regulate PV system voltage and current waveforms, ensuring grid stability during variable solar generation. The SAR MPPT connected SPV system tied utility grid also outperforms the P&O MPPT controller in active and reactive power management, allowing for 109.1 KW active power supply and 360.2 VAR reactive power supply by integrating modified STATCOM as compared to the P&O MPPT controller with standard PWM-VSI STATCOM which is supplying 108.1 KW and 865.3 VAR.

Purpose. The production of electricity from solar energy is necessary because of the global consumption of this energy. This article’s study is based on increased energy extraction by improving maximum power point tracking (MPPT). From different MPPT techniques proposed, the perturb and observe (P&O) technique is developed because of its low implementation cost and ease of implementation. Methods. A modified variable step-size P&O MPPT algorithm is investigated which uses fuzzy logic to automatically adjust step-size to better track maximum power point, compared with the conventional fixed step-size method. The variable step P&O improves the speed and the tracking accuracy. This controller is implemented on a boost DC-DC power converter to track the maximum power point. The suggested controlled solar energy system includes a boost converter, a voltage-source inverter, and a grid filter. The control scheme of a three-phase current-controlled pulse-width modulation inverter in rotating synchronous coordinate d-q with the proposed MPPT algorithm and feed-forward compensation is studied. Results. The photovoltaic grid-connected system controller employs multi-loop control with the filter inductor current of the inverter in the inner loop to achieve a fast dynamic response and the outer loop to control bus voltage for MPPT, the modeling, and control of three phase grid connected to photovoltaic generator is implemented in the MATLAB/Simulink environment and validated by simulation results.

The use of photovoltaic systems for clean electrical energy has increased. However, due to their low efficiency, researchers have looked for ways to increase their effectiveness and improve their efficiency. The Maximum Power Point Tracking (MPPT) inverters allow us to maximize the extraction of as much energy as possible from PV panels, and they require algorithms to extract the Maximum Power Point (MPP). Several intelligent algorithms show acceptable performance; however, few consider using Artificial Neural Networks (ANN). These have the advantage of giving a fast and accurate tracking of the MPP. The controller effectiveness depends on the algorithm used in the hidden layer and how well the neural network has been trained. Articles over the last six years were studied. A review of different papers, reports, and other documents using ANN for MPPT control is presented. The algorithms are based on ANN or in a hybrid combination with FL or a metaheuristic algorithm. ANN MPPT algorithms deliver an average performance of 98% in uniform conditions, exhibit a faster convergence speed, and have fewer oscillations around the MPP, according to this research.

Renewable energy resources are gaining a lot of popularity. Several researchers have worked on the tracking and extraction of energy from these sources. In the past few decades, among the available green energy resources, wind energy has been the most attractive option among the resources available. It is imperative to use the maximum power available in the wind to achieve the wind turbine (WT) operation at maximum power. The maximum power point tracking (MPPT) algorithms are a pioneer in this context. Many research papers are contributed in this domain which necessitates a thorough review while choosing an appropriate technique. This paper comprehensively focuses on reviewing different algorithms in the past and present for tracking maximum power point, and capturing maximized output power from the wind energy conversion system (WECS). In this paper, the algorithms are classified based on the direct and indirect power measurement, hybrid and smart algorithms for tracking maximum power point, and they are compared, considering the parameters like complexity, convergence speed, use of sensors, memory requirement, need for knowledge of system parameters, etc. The immense popularity of the different versions of perturb and observe (P&O) based algorithms due to their various features is evident from the literature. The review reveals that the hybrid maximum power point tracking algorithms can use the advantages of the conventional methods and eliminate their drawbacks.

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.

This paper proposes two new Maximum Power Point Tracking (MPPT) methods which improve the conventional Fractional Open Circuit Voltage (FOCV) method. The main novelty is a switched semi-pilot cell that is used for measuring the open-circuit voltage. In the first method this voltage is measured on the semi-pilot cell located at the edge of PV panel. During the measurement the semi-pilot cell is disconnected from the panel by a pair of transistors, and bypassed by a diode. In the second Semi-Pilot Panel method the open circuit voltage is measured on a pilot panel in a large PV system. The proposed methods are validated using simulations and experiments. It is shown that both methods can accurately estimate the maximum power point voltage, and hence improve the system efficiency.

Wind energy is one of the most important clean energies and the variable speed constant frequency technology is widely used in wind energy conversion systems. Maximum power point tracking (MPPT) is essential for a variable speed constant frequency wind power generation system. Concerning the current research on the MPPT algorithm, this paper studies the principle, characteristics, and reported improvement strategies of principal algorithms. Through the comparison of simulation results for selected control algorithms, the improved optimal torque control algorithm has been found to be the best MPPT algorithm for wind power generation systems because of its simplicity and efficiency. On this basis, further corresponding simulation runs are carried out to analyze the effect of the wind speed fluctuation characteristics on the systematic dynamic performance and power generation efficiency. The results show that the average wind speed, wind fluctuation frequency, and wind fluctuation amplitude can affect the performance of system operation and the efficiency of wind energy capture in different degrees, which has a great practical significance for the research of MPPT control strategy. Concerning the current research on the maximum power point tracking (MPPT) algorithm, this paper studies the principle, characteristics, and reported improvement strategies of principal algorithms. Through the comparison and analysis of simulation results, the improved optimal torque control algorithm has been found to be the best MPPT algorithm for wind power generation systems, and the average wind speed, wind fluctuation frequency, and wind fluctuation amplitude can affect the system performance and the energy capture efficiency in different degrees.