Experimental analysis of genetic algorithms based MPPT for PV systems

In order to use photovoltaic cell effectively and improve its photoelectric conversion efficiency, the maximum power point of photovoltaic generation system should be tracked rapidly and stably. In this paper after comparison and analysis common methods used in controller of photovoltaic systems such as Fuzzy and P&O, proposed an approach combined from FLC and particle swarm optimization algorithm (PSO) as an appropriate method to achieve maximum power point tracking (MPPT). Indeed Fuzzy logic control can cope with photovoltaic system using heuristic knowledge rules, but tuning the control parameters is not straightforward. PSO performs an on-line haphazard global search for input and output scaling factors of a PD-type fuzzy controller. The objective function of the PSO algorithm has been defined to minimize slope of P-V curve. The simulation results in SIMULINK of MATLAB indicate that proposed method can effectively eliminate the power oscillation around MPP and raise stability and reach steady state of the system.

Photovoltaic (PV) systems are increasingly used as a sustainable energy source, but their efficiency is significantly affected by environmental factors such as shading and temperature variations. This study proposes a novel Maximum Power Point Tracking (MPPT) algorithm based on the fusion of Support Vector Regression (SVR) and Incremental Conductance (INC) techniques, designed to enhance the performance of PV systems under partial shading conditions. The SVR model is employed to predict the power output of the PV system based on voltage and current measurements, leveraging its capability to handle non-linear relationships with high accuracy. The INC method is then integrated with the SVR predictions to dynamically adjust the operating point of the PV array, ensuring rapid and precise tracking of the maximum power point (MPP). The algorithm was implemented in MATLAB, and comprehensive simulations were conducted to evaluate its performance. Simulation results demonstrate that the SVR-INC fusion algorithm significantly improves the accuracy and stability of MPPT under varying shading conditions. The algorithm effectively reduces the tracking time to the MPP and minimizes power losses caused by partial shading. Compared to traditional MPPT methods, the proposed algorithm shows enhanced robustness and faster convergence, making it highly suitable for practical applications in real-world PV systems. Furthermore, the integration of SVR with INC provides a robust framework for dealing with the complexities of $P V$ power generation. The algorithm’s ability to adapt to changing environmental conditions ensures that the PV system operates at its maximum efficiency. The study also discusses the optimization of SVR parameters and the importance of datadriven approaches in enhancing MPPT performance. In conclusion, the proposed SVR-INC fusion MPPT algorithm offers a promising solution for improving the efficiency and reliability of PV systems under partial shading. The findings highlight the potential of this approach for future advancements in PV technology, contributing to the development of more efficient and resilient solar energy systems..

Generally, perturb and observe (P&O) technique is widely adopted in photovoltaic (PV) system to maximize the output power. In flyback inverter, the modulation index needs to be adjusted based on the P&O algorithm. However if the changing step size of modulation index (Δma) is too large, the fast MPP (Maximum Power Point) tracking can be achieved but the power oscillation around the MPP will be large. In contrary, the small changing step size results in long tracking time and small oscillation. Consequently, this paper proposes a technique to adjust the changing step size (Δma) of Flyback inverter to achieve both acceptable tracking time and low power oscillation. In the proposed technique, irradiance is adopted as the input of a neural network which is used to estimate the appropriate modulation step size. Simulation results confirm that the proposed neural network based inverter can find the appropriate changing step size (Δma) which is adequate for any irradiance conditions.

Purpose Silicon photovoltaics technology has drawbacks of high cost and power conversion efficiency. In order to extract the maximum output power of the module, maximum power point (MPP) is used by implying the nonlinear behavior of I-V characteristics. Different techniques are used regarding maximum power point tracking (MPPT). The paper aims to review the techniques of MPPT used in PV systems and review the comparison between Perturb and Observe (P&O) method and incremental conductance (IC) method that are used to track the maximum power and gives a comparative review of all those techniques. Design/methodology/approach A study of MPPT techniques for photovoltaic (PV) systems is presented. Matlab Simulink is used to find the MPP using P&O simulation along with IC simulation at a steady temperature and irradiance. Findings MATLAB simulations are used to implement the P&O method and IC method, which includes a PV cell connected to an MPPT-controlled boost converter. The simulation results demonstrate the accuracy of the PV model as well as the functional value of the algorithms, which has improved tracking efficiency and dynamic characteristics. P&O solution gave 94% performance when configured. P&O controller has a better time response process. As compared to the P&O method of tracking, the incremental conductance response rate was significantly slower. Originality/value In PV systems, MPPT techniques are used to optimize the PV array output power by continuously tracking the MPP under a variety of operating conditions, including cell temperature and irradiation level.

This study presents a new adaptive predictive error filter-based maximum power point tracker (MPPT) for a photovoltaic (PV) system. This MPPT is developed using the concept of an adaptive predictive filter (APEF) that consists of a one-tap finite impulse response. The filter step-size of the APEF is adapted using a recursive least-square (RLS) algorithm with an adaptive forgetting factor. The specialty of this MPPT is that it performs MPP tracking operation in a single step. It performs two functions: namely, MPP estimation adjusting operating point of a boost converter at MPP and then filtering of the PV voltage fluctuation after MPPT action. Thus, the proposed MPPT is compact and efficient with less computational complexities than existing MPPTs such as perturb and observe (P&O) and incremental conductance. The filter part of the proposed RLS-APEF-MPPT is a discrete PID-controller, where PD-part is tuned using pole-placement strategy and integral-term is fixed. The performances of the proposed RLS-APEF-MPPT were verified using experimentation on a prototype PV system. Its performances were compared with that of P&O, adaptive-P&O-MPPTs. From the observed results, it is confirmed that the proposed MPPT exhibits superior performance.

This paper presents an enhanced Maximum Power Point Tracking (MPPT) of Photovoltaic (PV) systems by means of Model Predictive Control (MPC) techniques. The PV array can feed power to the load through a DC/DC converter boosting the output voltage. Due to stochastic behavior of solar energy, MPPT control technique of PV arrays is required to operate at maximum power point. Extracting the maximum power from PV systems has been widely investigated within the literature. The main contribution of this paper is enhancement of the Incremental Conductance (INC) method through a fixed step predictive control under measured fast solar radiation variation. The proposed predictive control to achieve Maximum Power Point (MPP) speeds up the control loop since it predicts error before the switching signal is applied to the selected high gain multilevel DC-DC converter. Comparing the developed technique to the conventional INC method shows significant improvement in PV system performance. Experimental validation is presented using the dSpace CP 1103 to implement the proposed MPC-MPPT.

Photovoltaic (PV) systems are gradually becoming the key renewable source of electrical energy. But the nonlinear characteristics of the PV module require special techniques to harvest maximum power. The techniques used to track the Maximum Power Point (MPP) from the nonlinear I-V characteristics through power electronic circuits are known as Maximum Power Point Tracking (MPPT) techniques. Although enormous MPPT techniques have been anticipated in literature, each endures its own advantages and disadvantages. This paper proposes a novel hybrid technique to enhance the performance of Incremental conductance (InC) technique. Our proposed technique removes the limitations of InC under rapid changing environmental condition. It improves the efficiency and reduces the oscillation power loss under steady state. A hybrid combination of variable-step sized Incremental Conductance with Fractional Short Circuit Current (FSCC) technique is proposed in this research work. Initially, the FSCC forces the PV system to work near MPP and then InC technique track the exact peak. Matlab/Simulink software is used to implement the proposed hybrid technique. Finally, the results verify its superior performance under rapidly changing environmental conditions.

A maximum power point tracking (MPPT) controller was used to make the photovoltaic (PV) module operate at its maximum power point (MPP) under changing temperature and sunlight irradiance. Under partially shaded conditions, the characteristic power–voltage (P–V) curve of the PV modules will have more than one maximum power point, at least one local maximum power point and a global maximum power point. Conventional MPPT controllers may control the PV module array at the local maximum power point rather than the global maximum power point. MPPT control can be also implemented by using soft computing methods (SCM), which can handle the partial shade problem. However, to improve the robustness and speed of the MPPT controller, a hybrid MPPT controller has been proposed that combines two SCMs, the Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Matlab was used in a simulation of a GA-ACO MPPT controller where four SunPower SPR-305NE-WHT-D PV modules with a maximum power of 305.226 W connected in series were used under conditions of partial shade to investigate the performance of the proposed MPPT controller. The results obtained were analyzed and compared with others obtained under perturb and observe (P&O) MPPT and conventional ACO MPPT controllers were observed.

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 presents a single-phase standalone photovoltaic (PV) system with two stages of converters. The aim of this work is to track the maximum power point (MPP) so as to transfer the maximum available power to the load and to control the output current in order to feed the AC load by a sinusoidal current. These goals are attained by using the sliding mode to design control laws in order to command the boost DC-DC and the inverter switches. Thus, a maximum power point tracking (MPPT) and an output current controller based on the sliding mode are proposed. The innovative aspect of this work is to propose a standalone PV system with the controllers based only on the sliding mode control approach. The proposed system is modeled and simulated under MATLAB Simulink under fast variations of irradiance and temperature. Then, the obtained results using the suggested MPPT are compared to those using the incremental conductance (IC) method. These results demonstrate the superiority of the sliding mode MPPT in terms of the tracking speed, the efficiency, and the time of response. Moreover, the current controller provides an output current of high quality with a THD of 3.47%. Furthermore, for accurate results, these controllers are evaluated under the fluctuations of two daily climatic profiles (sunny and cloudy) and compared those of the IC method. The results illustrate that the sliding mode MPPT has the potential of generating more electrical energy than the IC MPPT with benefits of up to 13.02% for the sunny daily profile and 27.57% for the cloudy one.

One of the main problems with a solar photovoltaic (PV) system is the partial shading condition (PSC).This results in a significant reduction of the output power of a solar PV system.This paper mainly aims at proposing and validating a novel optimization technique namely the Genetic Algorithm (GA) for Maximum Power Point Tracking (MPPT) in case of PSC.In this study, an experimental examination utilizing a PV emulator highlights the effect of PSC on PV system performance.PSC was found to result in a 37% decrease in maximum power, a 38% decrease in fill factor, and a 60% decrease in efficiency.Metaheuristic techniques for P-V curves with several peaks can be used to track the maximum power point (MPP).GA is based on a metaheuristic methodology that has been applied to solve optimization problems in a variety of systems, such as PV systems with MPPT.With a convergence time of less than 2 ms, the suggested system can track the global MPP with 99% tracking efficiency.This demonstrates the improvement in tracking time and accuracy over traditional MPPT techniques.Additionally, the suggested system can also accomplish steady operation in dynamically changing environmental conditions and reduce the oscillations around MPP.

Currently, Photovoltaic (PV) energy is the most important energy resource in electrical power applications around the world since it is inexhaustible, clean and pollution free. As the output power from the PV arrays changing with solar irradiance and temperature, so it is necessary to operate the photovoltaic (PV) system at the maximum power point (MPP) to increase the power extraction from the solar panel. For this effective operation and maximum power extraction from a PV system, Maximum power point tracking (MPPT) algorithm plays a vital role by operating the PV energy system near the MPP. In this work, amongst several existing well established MPPT algorithms, the open voltage (OV) based algorithm is chosen for simulation of a PV system using Matlab / Simscape. As maximum power point (MPP) can be attained with a certain amount of load from the PV power system, so load matching is an important concept of MPPT algorithms. Here, the performances of this open voltage (OV) based algorithm method is evaluated for different values of solar radiation and battery was used as load to see the charging performance for the algorithm.

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

Partial shading conditions (PSCs) can significantly reduce the output energy produced by photovoltaic (PV) systems. Moreover, when such conditions occur, conventional and advanced maximum power point tracking (MPPT) systems fail to operate the PV system at its peak because the bypassing diodes may cause the PV system to become trapped at a low power point when they are in conduction mode. The PV system can be operated at the global maximum power point (MPP) with the help of global peak searching tools. However, the frequent use of these tools will reduce the output of PV systems since they force the PV system to operate outside its power region while scanning the I-V curve in order to determine the global MPP. Thus, the global peak searching tools should be deployed only when a PSC occurs. In this paper, a simple and accurate method is proposed for detecting PSCs by means of monitoring the sign of voltage changes (positive or negative). The method predicts a PSC if the sign of successive voltage changes is the same for a certain number of successive changes. The proposed method was tested on two types of PV array configurations (series and series–parallel) with several shading patterns emulated on-site. The proposed method correctly and timely identified all emulated shading patterns. It can be used to trigger the global MPP searching techniques for improving the PV system’s output under PSCs; furthermore, it can be used to notify the PV system’s operator of the occurrence of PSCs.

The paper introduces a novel Maximum Power Point Tracking (MPPT) approach for Photovoltaic (PV) systems which is based on Golden Section Optimization (GSO). The approach finds the Maximum Power Point (MPP) by a method known as interval shrinking. Initially, two points were extracted from the known search space, then evaluated and a new point was generated accordingly. At the given iteration, according to the evaluation observations, the algorithm has a new narrowed interval bounded by the new point and one of the original points. When the interval is short enough, the algorithm begins iterating and the PV is required to run at the average value of the last discovered interval without disrupting either another voltage or the duty cycle. This thing makes the PV system converge easily to the MPP and around the highest Power Point without voltage or power oscillations, thus minimizing energy loss. The study reveals that, under quickly changing conditions and PV modules, the MPPT algorithm uses a few arithmetic operations and monitors the MPP of PV systems rapidly and accurately.