Development of an Improved Bonobo Optimizer and Its Application for Solar Cell Parameter Estimation

Abstract

Recently, photovoltaic (PV) energy has been considered one of the most exciting new technologies in the energy sector. PV power plants receive considerable attention because of their wide applications. Consequently, it is important to study the parameters of the solar cell model to control and determine the characteristics of the PV systems. In this study, an improved bonobo optimizer (IBO) was proposed to improve the performance of the conventional bonobo optimizer (BO). Both the IBO and the BO were utilized to obtain the accurate values of the unknown parameters of different mathematical models of solar cells. The proposed IBO improved the performance of the conventional BO by enhancing the exploitation (local search) and exploration (global search) phases to find the best optimal solution, where the search space was reduced using Levy flights and the sine–cosine function. Levy flights enhance the explorative phase, whereas the sine–cosine function improves the exploitation phase. Both the proposed IBO and the conventional BO were applied on single, double, and triple diode models of solar cells. To check the effectiveness of the proposed algorithm, statistical analysis based on the results of 20 runs of the optimization program was performed. The results obtained by the proposed IBO were compared with other algorithms, and all results of the proposed algorithm showed their durability and exceeded other algorithms.

An Efficient Electric Charged Particles Optimization Algorithm for Numerical Optimization and Optimal Estimation of Photovoltaic Models

“…The flower pollination algorithms (FPA) were used to extract the global parameters of both the single-diode and the double-diode models based on the experimental data [15]. Additionally, other recent optimization algorithms were used to find the best values of PV parameters, including transient search optimization (TSO) [16], cuckoo search (CS) [17], whale optimization algorithm (WOA) [18], supply demand-based optimization (SDO) [19], salp swarm algorithm (SSA) [20], improved bonobo optimizer (IBO) [21], multiverse optimizer (MVO) [22], tree growth algorithm (TGA) [23], grey wolf optimization (GWO) [24], triple-phase teaching-learning-based optimization (TPTLBO) [25], ant lion optimization (ALO) [26], chaos game optimization (CGO) [27], Harris hawk optimization (HHO) [28], Rao algorithm [29], slime mould algorithm (SMA) [30], and hybrid techniques, such as hybrid adaptive TLBO with DE algorithm (ATLDE) [31], GWOCS [1], PSOGWO [32], Marine predator algorithm (MPA) [33], Coyote optimization [34], and Jaya algorithm and its variants [35]. Each technique has various strategies to achieve a certain objective, and the power of each technique depends on the precision of the estimated parameters, computation time, and computational complexity.…”

Energy and Exergy Analyses on Seasonal Comparative Evaluation of Water Flow Cooling for Improving the Performance of Monocrystalline PV Module in Hot-Arid Climate

“…Some of the absorbed solar irradiation is converted into electricity in a PV module, which operates on the principle of a semiconductor [5][6][7], while the remainder is either dissipated into the surroundings or absorbed by the module itself [8][9][10]. The absorbed energy raises the surface cumulative heat-induced temperature of the PV module, which decreases its output V-I (Voltage-Current) performance [11][12][13][14]. As a result, controlling the temperature of a PV module is critical, which can be accomplished through a variety of cooling techniques [14][15][16][17].…”

“…The absorbed energy raises the surface cumulative heat-induced temperature of the PV module, which decreases its output V-I (Voltage-Current) performance [11][12][13][14]. As a result, controlling the temperature of a PV module is critical, which can be accomplished through a variety of cooling techniques [14][15][16][17]. Either passive cooling or active cooling can be used [18][19][20][21][22].…”

Parameter Identification of Photovoltaic Cell Model Using Modified Elephant Herding Optimization-Based Algorithms

“…Table 1 reports some of the recent solvers that were applied for PV parameter estimation problems in the recent years [3], 2020 Projectile Search Algorithm [32], 2020 Grasshopper Optimizer [33], 2020 Backtracking Search Algorithm [5], 2020 Cuckoo Search Optimizer [34], 2020 Flower Pollination [35], 2021 Marine Predators Optimizer [6], 2018 Differential Evolution Algorithm [36], 2021 Newton-Raphson jointed with Heuristic Algorithm [37], 2020 Improved Wind-Driven Algorithm [9], 2021 Turbulent Flow of Water Optimizer [38], 2021 Supply-Demand Optimizer [39], 2019 Differential Evolution Algorithm [10], 2021 Forensic Optimizer [40], 2021 Improved Bonobo Optimizer [41], 2020 Slime Mold Optimizer [11], 2021 Gorilla Optimization Algorithm [42], 2013 Artificial Bee Swarm [43], 2020 Coyote Optimization Algorithm [21], 2021 Closed loop PSO and EHO [44], 2021 Hybrid Whale and PSO Optimizer [45], 2020 Adaptive Differential Evolution [31], 2019 Metaphor-Less Algorithms [46], 2021 Artificial Ecosystem Optimizer [47], 2019 Gray Wolf Optimization…”