Optimal Fractional-Order Fuzzy-MPPT for solar water pumping system

Abstract

This paper seeks to improve the efficiency of photovoltaic (PV) water pumping system using Fractional-order Fuzzy Maximum Power Point Tracking (FoF-MPPT) control and Gray Wolf Optimization (GWO) technique. The fractional calculus has been used to provide an enhanced model of PV water pumping system to, accurately, describe its nonlinear characteristics. Moreover, three metaheuristic optimizers are applied to tune the parameters of the proposed FoF-MPPT, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and the GWO. The FoF-MPPT is intensively tested and compared to the Perturb and Observe (PO), the Incremental Conductance (INC) and the FL-MPPT controllers. A MATLAB-Simscape based physical model of the PV water pumping system has been developed and simulated for different control techniques with the proposed optimization algorithms. The response of the PV water pumping systems is evaluated under rapidly changing weather conditions to prove the effectiveness of the optimized FoF-MPPT compared to the conventional algorithms. The reliability of the comparative study has been emphasized in terms of several transient tracking and steady- state performance indices under different operating conditions. The simulation results show the effective performance of the proposed metaheuristic optimized FL-MPPT and FoF-MPPT control under different climatic conditions with disturbance rejection and robustness analysis.

Real-Time Fuzzy-PID for Mobile Robot Control and Vision-Based Obstacle Avoidance

“…The concept of fuzzy logic control is not new; it arises from efforts to develop artificially intelligent decision-making and inference systems (Sain et al, 2022 ;Shalaby et al, 2021;Ghoudelbourk et al, 2021;Humaidi et al, 2021;Nasser et al, 2021;Mohanty et al, 2021;Fekik et al, 2021dFekik et al, , 2020bAhmadian et al, 2021;Abdelmalek et al, 2021;Ananth et al, 2021;Azar et al, 2013;Mjahed et al, 2020;Djeddi et al, 2019;Amara et al, 2019;Vaidyanathan and Azar, 2016;Kumar et al, 2018;Khettab et al, 2018;Pintea et al, 2018;Banu et al, 2017;Emary et al, 2014;Gharbia et al, 2014;Giove et al, 2013;Elshazly et al, 2013;Jothi et al, 2013). The structure of fuzzy inference is comparable to that of a human judgment process: incoming signals are evaluated subjectively and in a fuzzy manner.…”

A New Nonlinear Controller for the Maximum Power Point Tracking of Photovoltaic Systems in Micro Grid Applications Based on Modified Anti-Disturbance Compensation

“…This makes the PV cell operating point change as the irradiation or temperature and even the load change and it will not operate at the maximum power point (MPP). Therefore, a controlling technique has been proposed in the last decade under the name of maximum power point tracking (MPPT) [1][2][3][4][5][6][7][8][9]. Recently, several control techniques have been proposed as MPPT to extract and track the MPP such as the perturb and observe method (P&O) [10][11][12]; the incremental conductance method (IC) [10,13,14]; the constant voltage and constant current technique, which are known as the classical MPPT control technique [10]; and MPPT based on the control theory such as fuzzy control [15], neural network [16], optimal control [17], and robust control [18].…”

Design of neural network fractional‐order backstepping controller for MPPT of PV systems using fractional‐order boost converter

“…Recently, some satisfactory fractional controllers for PV system that guarantee a high efficiency level were developed, such as adaptive FO‐PID controller optimized by genetic algorithm (GA) 22 and optimal FO fuzzy logic controller 23 . To the best of our knowledge, there are no works about the use of FO converter in standalone PV systems.…”