Parameter Estimation of Photovoltaic Cell/Modules Using Bonobo Optimizer

Abdullrahman A Al-Shamma’A,Khalil Alsharabi,Hammed O Omotoso,Fahd A Alturki,Abdulaziz Alkuhayli,Abdullah M Noman,Hassan M H Farh

doi:10.3390/en15010140

Abstract

In this paper, a new application of Bonobo (BO) metaheuristic optimizer is presented for PV parameter extraction. Its processes depict a reproductive approach and the social conduct of Bonobos. The BO algorithm is employed to extract the parameters of both the single diode and double diode model. The good performance of the BO is experimentally investigated on three commercial PV modules (STM6-40 and STP6-120/36) and an R.T.C. France silicon solar cell under various operating circumstances. The algorithm is easy to implement with less computational time. BO is extensively compared to other state of the art algorithms, manta ray foraging optimization (MRFO), artificial bee colony (ABO), particle swarm optimization (PSO), flower pollination algorithm (FPA), and supply-demand-based optimization (SDO) algorithms. Throughout the 50 runs, the BO algorithm has the best performance in terms of minimal simulation time for the R.T.C. France silicon, STM6-40/36 and STP6-120/36 modules. The fitness results obtained through root mean square (RMSE), standard deviation (SD), and consistency of solution demonstrate the robustness of BO.

Highlights

The concern over increasing energy costs, losses in the contemporary energy system and the greenhouse gas effect have shifted the world focus towards renewable energy resources [1]
It is evident from the results that Bonobo optimizer (BO) achieved optimum parameter extraction with minimal oscillations as compared to manta ray foraging optimization (MRFO), artificial bee colony (ABO), particle swarm optimization (PSO), flower pollination algorithm (FPA) and supplydemand-based optimization (SDO)
It is evident from the results that BO achieved optimum parameter extraction with minimal oscillations as com9poafr2e2d to MRFO, ABO, PSO, FPA and SDO

Summary

Introduction

The concern over increasing energy costs, losses in the contemporary energy system and the greenhouse gas effect have shifted the world focus towards renewable energy resources [1]. Because of the non-linear model of the equations, and to ease computations, some assumptions are made which lead to inaccurate solutions [13,17] These methods are befitting for the SDM, their aptness is yet to be substantiated for the DDM [3]. To overcome the challenges associated with deterministic methods, several heuristic optimization approaches have been presented for the parameter extractions. Heuristic methods are notable for their global search operation and their efficacy towards handling non-linear functions without the requirements for gradient information Owing to their non-derivative nature, initial conditions are not required for the computation. For the first time, a simple and time-saving metaheuristic algorithm called Bonobo optimizer (BO) has been presented to extract the electrical parameters of PV modules models based on several series of experiments. The rest of the paper is organized as follows: Section 2 describes the mathematical model of a PV cell; Section 3 presents the BO optimization strategy; Section 4 presents the objective function; Section 5 explains the simulation results; Section 6 provides the conclusion

Mathematical Model of PV Module

PV Module Model

Promiscuous and Restrictive Mating Strategies

Consortship and Extra-Group Mating Strategies

Results and Discussion

Conclusions