Fuzzy based incremental conductance algorithm stabilized by an optimal integrator for a photovoltaic system under varying operating conditions

In the last decade, there has been a substantial surge in the advancement of research into the maximum power point tracking (MPPT) controller. The MPPT approaches, on the other hand, continue to be in high demand due to the ease and simplicity with which tracking techniques can be implemented on the maximum power point (MPP). Diverse MPPT approaches and their modifications from various literature are categorized and thoroughly explored in this work, which is divided into two sections. The discussions are centered on the primary goal of attaining the most extraordinary feasible MPPT technique that produces the best results at the lowest possible expense. In order to determine which MPPT approaches to use, evaluations from earlier literature are used to guide the decision. In this section, we will examine the evaluation of the MPPT technique in two sections. Previously, in Part I, we explored the MPPT techniques based on constant parameters and trial-and- error. Part II of this article will examine the MPPT technique, which is based on mathematical computation, measurement, and comparison, and the algorithm development that has occurred in recent years. Furthermore, this section’s assessment for selecting MPPT approaches is based on previous literature reviews. To aid with this selection, the following criteria for the MPPT approach are proposed: sensors and analog/digital requirements, costeffectiveness, simplicity, stability, efficiency, and tracking speed. This enables the reader to select the MPPT technique that is most appropriate for their application.

To mitigate problems like energy shortage and environmental degradation caused by conventional energy sources, it needs to shift from conventional energy to non-conventional energy sources. The solar photovoltaic (PV) technology emerges as one of the leading non-conventional energy sources. To operate a PV system at its maximum efficiency, it requires the operating module at maximum power output point (MPP). There are many conventional as well as advanced maximum power point tracking (MPPT) techniques developed and implemented for a solar PV system. This work provides a comparative analysis of the conventional and fuzzy logic controller (FLC)-based MPPT techniques for a solar PV system. In this study, performance of two conventional techniques is considered, i.e., perturb and observe (P & O) and incremental conductance (IC) MPPT technique. The analysis of the PV system is performed for different solar irradiance levels. The comparative study is evaluated for complexity, tracking time, efficiency, range of power, voltage, and current oscillations. The DC-DC boost converter is used for operation of the proposed PV system at MPP. The implementation of the entire system and control algorithms has been modeled in MATLAB Simulink.

This paper focuses on the intelligent techniques used for tracking the maximum power point of the solar photovoltaic (SPV) system for varying environmental conditions. The most widely used perturb and observe (P&O) maximum power point tracking (MPPT) technique is discussed briefly for the comparison with the intelligent techniques. The paper proposes control technique for the SPV system by using fuzzy logic controller (FLC)-based MPPT algorithm and the optimisation of its various parameters by genetic algorithm (GA). The performance of the FLC optimised with GA is compared with the P&O and the fuzzy-based MPPT technique. MATLAB/simulink software is used to design the different stages of the MPPT controllers. Simulation results reported that GA optimised FLC perform much better than the P&O and fuzzy logic-based MPPT controllers.

Substantial progress in solar photovoltaic (SPV) dissemination in grid-connected and standalone power generation systems has been witnessed during the last two decades. However, weather intermittency has a non-linear characteristic impact on solar photovoltaic output, which can cause considerable loss in the system’s overall output. To overcome these inevitable losses and optimize the SPV output, maximum power point tracking (MPPT) is mounted in the middle of the power electronics converters and SPV to achieve the maximum output with better precision from the SPV system under intermittent weather conditions. As MPPT is considered an essential part of the SPV system, up to now, many researchers have developed numerous MPPT techniques, each with unique features. A Google Scholar survey from 2015–2021 was performed to scrutinize the number of published review papers in this area. An online search established that on different MPPT techniques, overall, 100 review articles were published; out of these 100, seven reviews on conventional MPPT techniques under shading or partial shading and only four under non-uniform solar irradiance are published. Unfortunately, no dedicated review article has explicitly focused on soft computing MPPT (SC-MPPT) techniques. Therefore, a comprehensive review of articles on SC-MPPT techniques is desirable, in which almost all the familiar SC-MPPT techniques have to be summarized in one piece. This review article concentrates explicitly on soft computing-based MPPT techniques under non-uniform irradiance conditions along with their operating principles, block/flow diagram. It will not only be helpful for academics and researchers to provide a future direction in SC-MPPT optimization research, but also help the field engineers to select the appropriate SC-MPPT for SPV according to system design and environmental conditions.

This paper reviews the existing models, connection schemes and maximum power point tracking (MPPT) methods of PV arrays. The partial shading causes significant power losses. It is very difficult to maintain uniform irradiance over the entire PV array, especially for rooftop or building-integrated PV systems. The aspects such as connection scheme of PV array, shading pattern and maximum power point tracking (MPPT) techniques, etc., decide the power generated by the PV array. The conventional MPPT methods are only capable of reaching maximum power point (MPP) in identical insolation situations. Hence, conventional MPP tracking becomes inept due to the existence of several peaks in the P-V characteristics. There are different global maximum power point (GMPP) tracking techniques available in the literature that operate in partial shading scenarios. The reconfiguration of the PV array reduces the number of multiple peaks. This reduction in several peaks makes conventional MPP tracking techniques competent even under partial shading conditions with low implementation complexity and higher tracking speed. This paper can assist scientists in choosing a precise objective-based PV module model, connection schemes and MPP tracking methods out of numerous schemes available in the literature.

Solar photovoltaic (PV) system contributes a significant role in meeting power requirements and helps the decentralization of power generation and distribution. The output of the individual photovoltaic module depends on local temperature and irradiance. Since the variation of these parameters depends on the environmental factors, it is difficult to obtain maximum power in all instances. The maximum power point tracking (MPPT) technique is used for tracking the maximum power available at a particular instant. This paper presents the maximum power extraction from a photovoltaic system using Artificial Intelligence (AI) techniques. AI based methods provide a fast dynamic response with high accuracy and mitigate the effects of partial shading. A comparative study is performed for various AI based MPPT techniques such as Fuzzy, Artificial Neural Network (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS). PV module output is interfaced with the resistive load through the Z-Source inverter (ZSI). The ZSI boost the input voltage and provides an AC output voltage. The AI based MPPT techniques have been investigated with conventional Z-source Inverter (ZSI) and Switched Inductor Z-Source inverter (SL-ZSI). A simple boost control technique is employed to control, both conventional and SL Z-source inverter. The performance of the system is verified using MATLAB Simulink.

The best alternative to the conventional sources of energy is ever lasting solar energy and it is one among the cheapest and widely used energy. The present paper deals with the design and analysis of zeta converters, as it has been used as intermediate between voltage source inverter (VSI) and solar PV array. Maximum power point tracking (MPPT) technique has been used for gaining maximum efficiency from solar PV array. In this paper, Artificial Intelligence(AI) Technique based Fuzzy logic controller is used for better results. When compared to other methods of MPPT technique, Fuzzy logic based MPPT technique is best because it provides better results for randomly varying atmospheric conditions. In our proposed system, BLDC motor which has higher efficiency and noise less operation is considered. Maximum power loss of PV generator is well matched with the load characteristic of BLDC motor. Matlab/Simulink results carried out for the different topology of the DC-DC converters and the results are analyzed and compared. Keywords: Photovoltaic(PV) system, Maximum power point tracking (MPPT), Artificial Intelligence (AI) Technique, Fuzzy logic controller, BLDC motor, zeta converters.

Recently, many Maximum power point tracking (MPPT) techniques are proposed in the Photovoltaic (PV) systems. However, a comprehensive comparison of various MPPT techniques especially the dynamic tracking performance has not been made quantitatively for different working conditions. By reviewing different methods, the day-by-day operating evaluation method with practical meteorological data is used in this paper. Furthermore, three typical MPPT techniques including fixed step size P&O method, variable step size incremental conductance method, and hybrid step size Beta method, are selected in the performance evaluation. In order to improve the experimental repeatability and minimize the effect of random environmental factor such as partial shading, an indoor test system that consists of a DC-DC boost converter, a PV emulator and a dSPACE is used. The practical meteorological data are used for the evaluation these typical MPPT techniques and main experimental results are presented.

In the work a study is performed between different maximum power point tracking (MPPT) techniques for DC grid connected solar PV applications. To harness the maximum power from available input under changing weather conditions MPPT controller is used. Different MPPT techniques namely Perturb and Observe (P&O), Incremental Conductance (In &C), Fuzzy logic and Random Search Method (RSM) based MPPT techniques are implemented here. Due to simple computation, absence of derivatives RSM guarantees global convergence, unlike other methods. Simulation is performed in MATLAB/Simulink environment to identify efficient MPP tracker.

The current publication is directed to achieve a high-performance stand-alone PV system having the capability of tracking maximum output power, providing fixed output DC voltage, and attaining efficient system utilization, under different irradiation levels. A new maximum power point tracking (MPPT) approach integrating the incremental conductance algorithm and fuzzy logic control, and enhanced with PI-controller, was proposed to track maximum power. To provide fixed output DC voltage and approaching full system utilization, the PV system is equipped with a battery bank, electrolyzer; as a dump load, and buck-boost converter, with two controllers. The results of the proposed MPPT technique; modified incremental conductance (MINC), are compared with the corresponding results of three prevalently implemented MPPT algorithms: perturbed and observed (P&O), modified variable step-size P&O (VSZ-PO) and the ordinarily incremental conductance (INC). The highest output power, best tracking efficiency and best output power response are achieved by utilizing the proposed MPPT method. The results of the output voltage response and electrolizer on/off states confirm the ability of the PV scheme to provide fixed DC voltage and attain efficient system utilization, under varying irradiances.

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A PV array is composed of a number of series and parallel modules to meet the voltage, current and power requirements. In this paper, a modified Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) technique is presented. The modified P&O is used in the maximum power point tracking of a partially shaded PV system. The electrical performance of the proposed partial shaded P&O MPPT technique is compared to the conventional P&O MPPT technique. A MATLAB/SIMULINK model is applied to imitate the PV system under different climate conditions. The results show that the proposed method tracks the MPP efficiently with all levels of shading on the PV array.

This study offers a thorough examination of a photovoltaic (PV) system using a variety of maximum power point tracking (MPPT) methods, including fuzzy logic control (FLC), adaptive neuro-fuzzy inference systems (ANFIS), perturb and observe (P&O), and artificial neural networks (ANN). Optimizing power extraction from PV systems under various environmental circumstances, including temperature variations and irradiance, is the main goal of these MPPT algorithms. Despite its widespread use and affordability, the P&O algorithm may have performance issues in dynamic circumstances. By using fuzzy logic to adjust to non-linear changes in environmental conditions, FLC improves P&O and offers more dependable and seamless operation. Although they demand a large amount of data and processing power, ANN-based MPPT approaches provide sophisticated capabilities by predicting optimal operating points by learning from historical system actions. By fusing fuzzy logic and neural networks, ANFIS offers a reliable solution that can more accurately adjust in real time to changing circumstances. These algorithms' incorporation into a PV system allows for more flexible and effective power management, guaranteeing peak performance in a range of climatic conditions. By combining many MPPT techniques, hybrid approaches can further reduce the drawbacks of individual approaches and improve the overall dependability and efficiency of PV systems.

Dynamic global maximum power point tracking of the PV systems under variant partial shading using hybrid GWO-FLC

Presently, there has been a significant growth in the solar PV technology due to various factors such as clean technology, zero carbon emission and low maintenance. However, it becomes significant to extract the power in abundance from solar PV system due to its intermittent nature. To extract the significant power, a suitable maximum power point tracking (MPPT) technique has been presented. The MPPT technique is developed based on the hybrid fuzzy logic (FL) and improved incremental and conductance (IInC) method. The aim of developing hybrid FL-IInC technique for MPPT is to regulate duty cycle so that maximum power point (MPP) is achieved with stable and accurate outcomes during dynamic and steady state conditions. The IInC method was developed by regulating the duty cycle and step-size. of It was observed that proposed hybrid FL-IInC delivers accurate and stable results as compared with IInC method. During the model testing at the irradiance with 1000w/m2, the achieved MPPT efficiency was 99.96% as compared with the IInC with an outcome of 98.47% for the improved technique. Additionally, the settling time for the proposed hybrid FL-IInC was less compared with the IInC method. All the simulations were conducted with the MATLAB Simulink Platform. In future, the hybrid FL-IInC method can be employed with other conditions such as partial shading for better applicability