Modified Interactive Algorithm Based on Runge Kutta Optimizer for Photovoltaic Modeling: Justification Under Partial Shading and Varied Temperature Conditions

Abstract

The accuracy of characteristic the PV cell/module/array under several operating conditions of radiation and temperature mainly relies on their equivalent circuits sequentially; it is based on identified parameters of the circuits. Therefore, this paper proposes a modified interactive variant of the recent optimization algorithm of the rung-kutta method (MRUN) to determine the reliable parameters of single and double diode models parameters for different PV cells/modules. The results of the MRUN optimizer are validated via series of statistical analyses compared with five new meta-heuristic algorithms including aquila optimizer (AO) , electric fish optimizer (EFO), barnacles mating optimizer (BMO), capuchin search algorithm (CapSA), and red fox optimization algorithm (RFSO) moreover, twenty-five state-of the art techniques from literature. Furthermore, the identified parameters certainty is evaluated in implementing the characteristics of an entire system consists of series (S), and series-parallel (S-P) PV arrays with numerous dimensions. The considered arrays dimensions are three series (3S), six series (6S), and nine series (9S) PV modules. For the investigated arrays, three-dimensional arrays are recognized. The first array comprises 3S-2P PV modules where two parallel strings (2P) have three series modules in each string (3S). The second array consists of six series-three parallel (6S-3P) PV modules, and the third one has nine series-nine parallel (9S-9P) PV modules. The results prove that the proposed algorithm precisely and reliably defines the parameters of different PV models with root mean square error and standard deviation of 7.7301e −4 ± 4.9299e −6 , and 7.4653e −4 ± 7.2905e −5 for 1D, and 2D models, respectively meanwhile the RUN have 7.7438e −4 ± 3.5798e −4 , and 7.5861e −4 ± 4.1096e −4 , respectively. Furthermore MRUN provided extremely competing results compared to other well-known PV parameters extraction methods statistically as it has .

Inventory Management Optimization of Green Supply Chain Using IPSO-BPNN Algorithm under the Artificial Intelligence

“…Yousri et al proposed an interactive variant of the RUN optimization algorithm to determine the reliable parameters of the single-diode and double-diode model parameters of different photovoltaic cells/modules. The results show that this method provides highly competitive results compared with other well-known parameter extraction methods [31].…”

Chaotic Enhanced Genetic Algorithm for Solving the Nonlinear System of Equations

“…In our future works, three directions will be concentrated: (i) implementing more modifications for CEGA and assessing their impact on optimization results, (ii) applying CEGA to solve optimization problems in different fields, and (iii) using other metaheuristic algorithms to solve this kind of problems, such as particle swarm optimization [ 41 ], ant colony optimization [ 42 ], artificial bee colony (ABC) Algorithm [ 43 ], krill herd [ 44 ], monarch butterfly optimization (MBO) [ 45 ], earthworm optimization algorithm (EWA) [ 46 ], elephant herding optimization (EHO) [ 47 ], moth search (MS) algorithm [ 48 ], slime mould algorithm (SMA) [ 49 ], hunger games search (HGS) [ 50 ], Runge Kutta optimizer (RUN) [ 51 ], colony predation algorithm (CPA) [ 52 ], and harris hawks optimization (HHO) [ 53 ].…”

Parameter Extraction of Single, Double, and Triple‐Diode Photovoltaic Models Using the Weighted Leader Search Algorithm

“…Rung-Kutta optimizer [ 32] R. [ 34] Schutten Solar STM6-40 parameter extraction problem is defined, PV models and objective functions are given. Section 3 describes the solution tools for this problem.…”