An Improved Fuzzy Logic Controller Design for PV Inverters Utilizing Differential Search Optimization

Abstract

This paper presents an adaptive fuzzy logic controller (FLC) design technique for photovoltaic (PV) inverters using differential search algorithm (DSA). This technique avoids the exhaustive traditional trial and error procedure in obtaining membership functions (MFs) used in conventional FLCs. This technique is implemented during the inverter design phase by generating adaptive MFs based on the evaluation results of the objective function formulated by the DSA. In this work, the mean square error (MSE) of the inverter output voltage is used as an objective function. The DSA optimizes the MFs such that the inverter provides the lowest MSE for output voltage and improves the performance of the PV inverter output in terms of amplitude and frequency. The design procedure and accuracy of the optimum FLC are illustrated and investigated using simulations conducted for a 3 kW three-phase inverter in a MATLAB/Simulink environment. Results show that the proposed controller can successfully obtain the desired output when different linear and nonlinear loads are connected to the system. Furthermore, the inverter has reasonably low steady state error and fast response to reference variation.

A Novel Quantum-Behaved Lightning Search Algorithm Approach to Improve the Fuzzy Logic Speed Controller for an Induction Motor Drive

“…The Mamdani method is used in this study because of its simple structure and design. The fuzzy rule consists of the if-then linguistic term, and the output MFs are operated by the fuzzy rule that is related to both the inputs pe, deq and the outpu pω sl q [11,16,41]. The details of the 49 rules are shown in Table 1 and in the following equations:…”

“…However, the conventional FLC has a disadvantage in selecting the boundaries of the input and output of MFs [16,41]. This section explains how the improved FLC utilizes QLSA to identify the best boundaries of the input/output of MFs.…”

Fuzzy logic speed controller optimization approach for induction motor drive using backtracking search algorithm

“…FLC controllers have replaced other controllers owing to the former's improved control of the speed and mechanical load, which allows such controllers to exhibit excellent performance in terms of transient reduction and control [8,18]. Thus, several applications are implemented to enhance the performance of the temperature control precision and robustness of the chamber cooling system [19].…”

“…However, the performance of FLCs depends on the membership functions (MFs), the number of rules and rule basis. These variables are determined by a trial and error procedure, which is time consuming [18,21]. Therefore, to overcome these limitations, FLC design optimization techniques uses differential search algorithm to develop FLC and control of photovoltaic inverters [18].…”

“…These variables are determined by a trial and error procedure, which is time consuming [18,21]. Therefore, to overcome these limitations, FLC design optimization techniques uses differential search algorithm to develop FLC and control of photovoltaic inverters [18]. In [22], particle swarm optimization (PSO) is proposed to enhance FLC for maximum power point tracking (MPPT) in a grid-connected photovoltaic inverter.…”