Enhanced Vibrating Particles System Algorithm for Parameters Estimation of Photovoltaic System

Abstract

To evaluate the performance of a photovoltaic panel, several parameters must be extracted from the photovoltaic. These parameters are very important for the evaluation, monitoring and optimization of photovoltaic. Among the methods developed to extract photovoltaic parameters from current-voltage (I-V) characteristic curve, metaheuristic algorithms are the most used nowadays. A new metaheuristic algorithm namely enhanced vibrating particles system algorithm is presented here to extract the best values of parameters of a photovoltaic cell. Five recent algorithms (grey wolf optimization (GWO), moth-flame optimization algorithm (MFOA), multi-verse optimizer (MVO), whale optimization algorithm (WAO), salp swarm-inspired algorithm (SSA)) are also implemented on the same computer. Enhanced vibrating particles system is inspired by the free vibration of the single degree of freedom systems with viscous damping. To extract the photovoltaic parameters using enhanced vibrating particles system algorithm, the problem can be set as an optimization problem with the objective to minimize the difference between measured and estimated current. Four case studies have been implemented here. The results and comparison with other methods exhibit high accuracy and validity of the proposed enhanced vibrating particles system algorithm to extract parameters of a photovoltaic cell and module.weather conditions change, analytical methods become ineffective [18].Numerical extraction techniques based on some algorithm fit the points on the PV characteristic curve. Compared to the analytical method, an accurate result can be attained since the algorithm tries to consider all points on the characteristic curve [19]. In the literature, the Newton-Raphson method is the most used [19] [20] [21]; In [22], a numerical method is proposed for modeling and the simulation of PV. The method finds the five parameters from the current-voltage characteristic by using three points of the curve (maximum power, open circuit and short circuit). In [23], the Levenberg Marquardt algorithm is

A combination of Newton-Raphson method and heuristics algorithms for parameter estimation in photovoltaic modules

“…This section presents various experiments results including the approximation formulation, the accurate formulation results that are presented in section 5.1 and Section 5.2 respectively, and the 10 recent algorithms published: EVPS [ 6 ], TVACPSO [ 50 ], IMFO [ 35 ], IJAYA [ 19 ], ITLBO [ 25 ], CAO [ 37 ], SOS [ 39 ], MLBSA [ 38 ], MADE [ 36 ], GAMS [ 3 ]. The experiment of these algorithms was made based on the well known set of RTC France solar cell, with various parameters presented at Table 1 .…”

“…Numerous available algorithms in literatures portray different modelling techniques such as conventional GA and basic progressive computing techniques. Double PV parameters estimation for diode model was done using: improved the Optimization of Harris hawks (HHO) [ 15 ], improved adapted differential and evolution with repairing crossover rate (RcrIJADE) [ 16 ], genetic algorithm (GA) [ 17 ], algorithm of the enhanced vibration of particles system (EVPS) [ 6 ], artificial algorithm of bee swarm optimizations (ABSO) [ 18 ] and algorithm optimization of the improved JAYA (IJAYA) [ 19 ]. In addition we have the use of self-adaptive teaching-learning-based optimization (SATLBO) [ 20 ], improved optimization of whale algorithm (IWAO) [ 21 ] algorithm of the improved shuffled and complex evolutions (ISCE) [ 22 ], algorithm of hybrid pollination of flower (GOFPANM) [ 23 ] and grasshopper optimization algorithm (GOA) [ 24 ], the improved teaching and learning based optimization (ITLBO) [ 25 ], the algorithm of hybrid cuckoo search (HBCS) [ 26 ].…”

Optimal PV Parameter Estimation via Double Exponential Function-Based Dynamic Inertia Weight Particle Swarm Optimization

“…Optimal parameters obtained using DEDIWPSO for the Photo-watt module (PWP201), RTC France silicon solar cell and a practical PV system are presented in this section. The I-V data for the first two test systems were taken from [24], and have been utilized by various researchers [38][39][40][41]. Inertia weight was initially set at 0.8, and then exponentially decreased following Equation (12).…”

Parameter Estimation of the Photovoltaic System Using Bald Eagle Search (BES) Algorithm

“…In practical applications, a single-diode model (SDM) and double-diode model (DDM) are commonly used to describe the nonlinear voltage-current characteristics of photovoltaic systems. This section describes the properties of each of these two models [3,52,57].…”