Improving maximum power point tracking of partially shaded photovoltaic system by using IPSO-BELBIC

Abstract

Solar photovoltaic (PV) arrays in remote applications are often related to the rapid changes in the partial shading pattern. Rapid changes of the partial shading pattern make the tracking of maximum power point (MPP) of the global peak through the local ones too difficult. An essential need to make a fast and efficient algorithm to detect the peaks values which always vary as the sun irradiance changes. This paper presents two algorithms based on the improved particle swarm optimization technique one of them with PID controller (IPSO-PID), and the other one with Brain Emotional Learning Based Intelligent Controller (IPSO-BELBIC). These techniques improve the maximum power point (MPP) tracking capabilities for photovoltaic (PV) system under partial shading circumstances. The main aim of these improved algorithms is to accelerate the velocity of IPSO to reach to (MPP) and increase its efficiency. These algorithms also improve the tracking time under complex irradiance conditions. Based on these conditions, the tracking time of these presented techniques improves to 2 msec, with an efficiency of 100%.

Recent developments of MPPT techniques for PV systems under partial shading conditions: a critical review and performance evaluation

“…Metaheuristic search is used in the first stage to identify the vicinity of the global peak; after that, the FL is used to track the GMPP accurately. An improved PSO with brain emotional learning-based intelligence (IPSO-BELBI) is used for the same reason [91]. Another group of researchers opted to combine two or more metaheuristic search methods: WO-DE [92], PSO-SFLA [93], WOA-SA [94], FLC-FFA [95], and DE-PSO [15].…”

Biologically inspired adaptive intelligent secondary control for MGs under cyber imperfections

“…), which is directly sent from the thalamus to the amygdala, is defined as where is the weight and the corresponding update law is as follows: Several techniques have been adopted for tuning the BELBIC parameters. For instance, genetic algorithm is adopted for optimally tuning BELBIC parameters in [31] while a particle swarm optimisation‐based approach is implemented in [32]. Jafari et al in [33] adopted the clonal selection algorithm to obtain BELBIC parameters, where it has been successfully applied for controlling a single‐link flexible joint manipulator.…”

“…Several techniques have been adopted for tuning the BELBIC parameters. For instance, genetic algorithm is adopted for optimally tuning BELBIC parameters in [31] while a particle swarm optimisation-based approach is implemented in [32]. Jafari et al in [33] adopted the clonal selection algorithm to obtain BELBIC parameters, where it has been successfully applied for controlling a single-link flexible joint manipulator.…”

Biologically-Inspired Intelligent Flocking Control for Networked Multi-UAS with Uncertain Network Imperfections

“…where V th is the weight and the corresponding update law is the same as Equation 10. Several techniques have been adopted for tuning the BELBIC parameters [26,[31][32][33][34][35]. In this paper, to significantly reduce the computational complexity, a heuristic approach is utilized for tuning the BELBIC parameters.…”