Maximum Power Point Tracking Techniques For Photovoltaic System Research Articles

A new intelligently optimized model reference adaptive controller using GA and WOA-based MPPT techniques for photovoltaic systems

Recently, the integration of renewable energy sources, specifically photovoltaic (PV) systems, into power networks has grown in significance for sustainable energy generation. Researchers have investigated different control algorithms for maximum power point tracking (MPPT) to enhance the efficiency of PV systems. This article presents an innovative method to address the problem of maximum power point tracking in photovoltaic systems amidst swiftly changing weather conditions. MPPT techniques supply maximum power to the load during irradiance fluctuations and ambient temperatures. A novel optimal model reference adaptive controller is developed and designed based on the MIT rule to seek global maximum power without ripples rapidly. The suggested controller is also optimized through two popular meta-heuristic algorithms: The genetic algorithm (GA) and the whale optimization algorithm (WOA). These meta-heuristic approaches have been exploited to overcome the difficulty of selecting the adaptation gain of the MRAC controller. The reference voltage for MPPT is generated in the study through an adaptive neuro-fuzzy inference system. The suggested controller’s performance is tested via MATLAB/Simulink software under varying temperature and radiation circumstances. Simulation is carried out using a Soltech 1sth-215-p module coupled to a boost converter, which powers a resistive load. Furthermore, to emphasize the recommended algorithm’s performance, a comparative study was done between the optimal MRAC using GA and WOA and the conventional incremental conductance (INC) method.

A comparison of FLC MPPT techniques and HC MPPT techniques for photovoltaic systems

This study aims to examine the behaviour of various maximum power point tracking (MPPT) methods used with PV systems. The methodologies of hill climbing (HC) and fuzzy logic controller (FLC) are assessed in this paper. PV module and DC/DC boost converter models were simulated using PSIM and Simulink software with various MPPT strategies. The PSIM and Simulink software programmes were founder throughout the FLC MPPT method’s development. To employ each software for specific system components, the co-simulation was conducted. The effectiveness of the various MPPT approaches was assessed in response to rapidly varying weather conditions. The results show that for most of the tested MPPT approaches’ usual working ranges, FLC outperforms the HC MPPT approach in transient behavior and constant.

A conceptual framework and a Review of AI-Based MPPT Techniques for Photovoltaic Systems

Several Maximum Power Point Tracking (MPPT) techniques based on various Artificial Intelligence (AI) algorithms were recently developed due to the current availability of powerful computation controllers and adaptability of AI algorithms and their characteristic in handling non-linear problems. AI algorithms are perfectly suited to handle the problem of adverse conditions of rapid irradiance change and partial shading that the PV systems suffer. This paper presents a conceptual framework of the MPPT for photovoltaic systems and a comprehensive review of the current AI-based MPPT techniques. The paper also covers MPPT components, modeling, characteristics, affecting factors, and categories. The performance of different AI algorithms is evaluated and categorized based on many criteria including system complexity, tracking speed, cost, efficiency, accuracy, hardware implantation, sensory parameters, response to Partial Shading Conditions (PSC), etc. Finally, a summarizing comparison of performance of the main MPPT techniques is presented.

Novel MPPT techniques for photovoltaic systems under uniform irradiance and Partial shading

In PV systems, the non-uniform irradiance and diversified unpredictable weather conditions fall into the category of Partial Shading (PS). Under PS, it is challenging for PV systems to obtain the maximum output through Maximum Power Point Tracking (MPPT), i.e., the parameters of the controller are adjusted online to yield the maximum power. In the literature, various techniques have been proposed to track the MPP (Maximum Power Point) under the uniform irradiance. On the contrary, few techniques have been proposed to efficiently track MPP under PS. In this paper, a few novel MPPT techniques have been proposed, which include Adaptive Cuckoo Search Optimization Algorithm (ACOA), General Regression Neural Network GRNN) with Fruit fly Optimization algorithm (FFOA), and Dragonfly Optimization Algorithm (DFO) to track the MPP under various weather condition. The proposed techniques enhance the performance of the PV system, save the computational time and greatly reduce the oscillation around the global maximum power point. For the validation of the proposed techniques, comparative analysis of their results with the Bio-inspired Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CS), Artificial Bee Colony Algorithm (ABC) and PSO Gravitational search Optimization (PSOGS) is presented. The comparison shows that the proposed techniques are better in term of quick power tracking, stability, and high efficiency under various weather conditions. The comparison also demonstrates that the proposed techniques can efficiently locate the GM (global maxima) under the PS and Dynamic Partial Shading (DPS) conditions. Furthermore, statistical analysis is presented to check the stability, sensitivity and robustness of the proposed techniques.

Critical Study of Several MPPT Techniques for Photovoltaic Systems

This paper presents a critical study of Maximum Power Point Tracking (MPPT) techniques for photovoltaic systems. After a brief introduction of the key factors for power extraction of photovoltaic panels, a review of commonly used MPPT techniques is presented and detailed with a holistic approach. Then, a comparison of these techniques is made according to several parameters such as robustness, response time, cost ... In the last part, the advantages and disadvantages of each of the MPPT techniques considered are presented. This article can serve as a quick guide to panel selection and MPPT technique for specific applications.

Experimental investigations and comparison of various MPPT techniques for photovoltaic system

This paper deals with Matlab/Simulink and experimental investigations of various maximum power point tracking (MPPT) techniques namely incremental conductance (Inc), perturb and observation (P&O), constant voltage control (CVC) method, and introduction of a novel reference power (Pref) method for extracting the maximum power from the solar photovoltaic (PV) system. The complete system model along with these MPPT methods are developed in Matlab/Simulink and simulation results are obtained during sudden increase in irradiation of 1000 W/m2, and verified experimentally. These MPPT methods are also implemented on Inverse-Sepic converter, which claims to extract maximum power from the PV system. Various experimental observations are taken to access the performance of these MPPT techniques such as settling time of the full load current under sudden exposure of irradiation level of 1000 W/m2, performance during step increase in irradiation level (from 500 W/m2 to 1000 W/m2) and vice versa. Extensive results are taken to compare these methods. The proposed Pref method is found to have lowest settling time to stabilize the load current as 520 ms, whereas, Inc method takes 1.24 s. Further, the efficiency of Inverse-Sepic converter with Pref method achieves the highest efficiency of 95.26%, whereas, it extracts lowest efficiency on same input as 90.77% with P&O method. The overall performance of the proposed Pref method is found to be superior as compared to other discussed MPPT methods as verified by experimental results.

Investigation of ANN-GA and Modified Perturb and Observe MPPT Techniques for Photovoltaic System in the Grid Connected Mode

The output characteristics of Photovoltaic (PV) arrays are nonlinear and change with the cell's temperature and solar radiation. Maximum Power Point Tracking (MPPT) methods are used to maximize the PV array output power by tracking continuously the maximum power point (MPP). This paper presents an integrated offline Genetic Algorithm (GA) and artificial neural network (ANN) to track the solar power optimally based on various operation conditions due to the uncertain climate change. Data are optimized by GA and then these optimum values are used in neural network training. The obtained results show minimal error of MPP, optimal voltage (Vmpp) and superior capability of the suggested method in the MPPT. The simulation results are presented by using Matlab/Simulink and show that ANN-GA controller of grid-connected mode can meet the need of load easily and have fewer fluctuations around the maximum power point; also, this method has well regulated PV output power and it produces extra power rather than Modified Perturb&Observe (MP&O) method for different conditions. Moreover, to control both line voltage and current, a grid side P-Q controller has been applied.

A comprehensive comparison of different MPPT techniques for photovoltaic systems

This paper aimed to study the behavior of different maximum power point tracking (MPPT) techniques applied to PV systems. In this work, techniques such as hill climbing (HC), incremental conductance (INC), perturb-and-observe (P&O), and fuzzy logic controller (FLC) are assessed. A model of PV module and DC/DC boost converter with the different techniques of MPPTs was simulated using PSIM and Simulink software. Co-simulation between PSIM and Simulink software packages is used to establish FLC MPPT technique. The co-simulation is done to take advantage of each program to handle certain part of the system. The response of the different MPPT techniques is evaluated in rapidly changing weather conditions. The results indicate that, FLC performed best among compared MPPT techniques followed by P&O, INC, and, HC MPPT techniques in both dynamic response and steady-state in most of the normal operating range.