Marine Predators Algorithm for Parameters Identification of Triple-Diode Photovoltaic Models

Abstract

The precise electrical modeling of photovoltaic (PV) module is crucial due to the large-scale permeation of PV power plants into electric power networks. Therefore, a triple-diode photovoltaic (TDPV) model is presented to address all PV losses. However, the TDPV is mathematically modelled by a nonlinear I-V behavior, including nine-parameters that cannot be directly determined from the PVs datasheet due to the lack data offered by the PV manufacturers. This article presents a new application of the marine predators algorithm (MPA) to properly extract the electrical parameters of the TDPV model of a PV panel. The validity of the MPA-based TDPV model is widely appraised by the numerical analyses, which are carried out under various temperatures and solar irradiations. The optimal nine-parameters achieved using the MPA are compared with that realized by different optimization approaches-based PV model. For a realistic study, the numerical results and the measured data are compared for the marketable Kyocera KC200GT and Solarex MSX-60 PV panels. The efficacy of the MPA-based TDPV model is properly executed by checking its current error with that obtained from various models. With the MPA technology, a highly accurate model of any marketable PV module can be attained, which represents a new contribution to the sector of PV power systems. INDEX TERMS Marine predators algorithm, photovoltaic modeling, photovoltaic power systems, solar energy, triple-diode model.

Identification of Parameters in Photovoltaic Models through a Runge Kutta Optimizer

“…The losses in DDSCM can be identified by the losses of the quasi-neutral and space charge regions. The losses in TDSCM can be identified by the losses of the quasi-neutral, space charge and defect regions [15,16].…”

A Novel Solution Methodology Based on a Modified Gradient-Based Optimizer for Parameter Estimation of Photovoltaic Models

“…Conversely, the numerical (metaheuristic) evolutionary and hybrid algorithms are capable of escaping from local optima and reaching the global optimum solution easily. As per the literature, there are many metaheuristic optimization algorithms used in the estimation of PV parameters estimation, such as: Particle Swarm Optimization (PSO) [12], Artificial Bee Colony (ABC) [13], Real Coded Genetic Algorithm (RCGA) [14], Cuckoo Search (CS) [15] , Biogeography-based Heterogeneous Cuckoo Search (BHCS) [16], Firefly Algorithm (FA) [17], Moth-Flame Optimization Algorithm (MFOA) [18], Bee Pollinator Flower Pollination Algorithm (BPFPA) [19], Pattern Search (PS) [20], Harmony Search (HS) [21], Fish Swarm Algorithm (FSA) [22], Ant Lion Optimizer (ALO) [23], Water Cycle Algorithm (WCA) [24], Jaya algorithm [25], Hybridized Interior Search Algorithm (HISA) [26], Artificial Immune System (AIS) [27], Salp Swarm Algorithm (SSA) [28], Artificial Biogeography based Optimization Algorithm with Mutation (BOA-M) [29], Elephant Herd Algorithm (EHA) [30], an Artificial Bee Colony-Differential Evolution (ABC-DE) [31], improved adaptive Nelder-Mead Simplex(NMS) hybridized with ABC algorithm, hybrid EHA-NMS [32], Improved Adaptive DE (IADE) [33], Chaotic Asexual Reproduction Optimization (CARO) [34], Improved Shuffled Complex Evolution (ISCE) [35], Heterogeneous Comprehensive Learning Particle Swarm Optimizer (HCLPSO) [36], Mutative-scale Parallel Chaos Optimization Algorithm (MPCOA) [37], Artificial Ecosystem optimization [38], Marine Predators Algorithm (MPA) [39], Enhanced Teaching-Learning-Based Optimization (ETLBO) algorithm [40], Coyote Optimization Algorithm (COA) [41], Harris Hawk Optimization (HHO) [42], Sunflower Optimization (SFO)…”

Multi-Regional Optimal Power Flow Using Marine Predators Algorithm Considering Load and Generation Variability

“…MPA is an efficient meta-heuristic with many benefits, including the reduced number of variables configured, compact structure, noticeable convergence velocity, near-global approach, consistency, problem independence, and gradient-free nature [41]. The MPA has been implemented in [42] to precisely calculate the unidentified electrical nine variables of the triple-diode photovoltaic (TDPV) configuration of a PV module. Furthermore, the MPA is being used in the prediction of confirmed Covid-19 cases [43].…”