Arithmetic optimization algorithm based maximum power point tracking for grid-connected photovoltaic system

Abstract

This paper suggests an optimal maximum power point tracking (MPPT) control scheme for a grid-connected photovoltaic (PV) system using the arithmetic optimization algorithm (AOA). The parameters of the proportional-integral (PI) controller-based incremental conductance (IC) MPPT are optimally selected using AOA. To accomplish this study, a 100-kW benchmark PV system connected to a medium distribution utility is constructed and analyzed employing MATLAB/SIMULINK. The optimization framework seeks to minimize four standard benchmark performance indices, then select the best of the best among them. To verify the efficacy of the recommended methodology, a comprehensive comparison is conducted between AOA-based PI-IC-MPPT, modified incremental conductance MPPT (MIC), grey wolf optimization (GWO), genetic algorithm (GA), and particle swarm optimization (PSO)-based MPPT. The proposed control approach has achieved a reduction of 61, 3, 4.5, and 26.9% in the rise time and a decrease of 94, 84.7, 86.6, and 79.3% in the settling time compared with MIC, GWO, GA, and PSO in extracting MPPT of the proposed system, respectively.

Comparison of Bioinspired Techniques for Tracking Maximum Power under Variable Environmental Conditions

“…In the abovementioned equations (20)(21)(22), x1 represents the panel voltage, x2 represents the panel current, and Fx is the maximum power obtained from the panel under different environmental conditions. All the dependent parameters for ABC, PSO, GA, and JFO algorithms are given in Table 5, in which many of the parameters are kept the same as it was in case study 1 (1Soltech-1STH-215P module), except the upper limit taken for the case study 2 (SolarWorld Industries GmbH Sunmodule plus SW 245 poly) at STC, PSC, and step-change of irradiances.…”

“…JFO tracking efciency in all 17 compares the proposed jellyfsh optimization with some existing techniques. Diferent users have used diferent parameters and specifcations to prove that their technique is better than the existing one, so diferent comparison parameters are considered such as tracking time [16], RMSE [14,15,18], efciency [13,17,22,24,27,29,31,[34][35][36], MAE [20], accuracy [41], reduced rise time [21], settling time/ tracking time [21,29,35], and maximum power [28,35,36]. In this paper, maximum power tracked, maximum efciency, total execution time, convergence speed, complexity, parameter, steady-state oscillation, stability, sensitivity, tracking ability, and economy have been compared [10,12,13,23,27]; FPA performs better with a small margin than PSO in terms of maximum power [28].…”

“…A few of the well-known optimization techniques are artifcial immune system (AIS) [8], ant colony optimization (ACO) [5,8,[12][13][14], moth-fame optimization (MFO) [12,15], whale optimization algorithm (WOA) [12], butterfy optimization (BFO) algorithm [12], shufed frog leaping algorithms (SFLA) [12], slap swarm algorithm [12], frefy algorithm (FA) [12,16,17], artifcial bee colony (ABC) [5,8,9,12,13,[16][17][18][19][20], bat-search algorithm (BA) [12,17], grey wolf optimization (GWO) [5,12,13,15,17,21,22], genetic algorithm (GA) [8, 12-14, 21, 23], fower pollination optimization (FPO) [12,16,24], JAYA [16], DE [8,16], cuckoo search (CS) [5,6,12,13,15,16,…”

“…Tese are optimization techniques that have been utilized for comparing diferent factors in terms of tracking time [16,21,24,29], tracking efciency [13,14,16,20,22,24,29,31], root mean square error [12,14,15,18,20], and mean absolute error [20]. Some of the new optimization techniques that have been recently introduced in the PV system analysis in 2022 and 2023 are specifed as seagull optimization algorithm [27], African vulture optimization [30], jellyfsh optimization algorithm [32], arithmetic optimization algorithm [21], reptile search algorithm [22], particle swarm optimization with butterfy optimization algorithm [29], DGBCO [33], EVO [34], GRNN-OPA [35], MRO [36], and many others that are utilized in diferent felds of engineering.…”

“…PSO and FPA are compared with ten (10) samples for PSC and it is concluded that the FPA produces 86.03 W power output compared to the power output in PSO of 85.5 W under PSC in the PV panel. Te authors in [21] introduced AOA-based PI-IC-MPPT, which gives better results than MIC, GWO, GA, and PSO in terms of reducing rise time and settling time. Te authors in [22] used the RAS optimization technique and proved that RSA gives better efciency than DOA, GWO, WOA, and PSO.…”