Improved Metaheuristic Optimization Algorithm Applied to Hydrogen Fuel Cell and Photovoltaic Cell Parameter Extraction

Abstract

As carriers of green energy, proton exchange membrane fuel cells (PEMFCs) and photovoltaic (PV) cells are complex and nonlinear multivariate systems. For simulation analysis, optimization control, efficacy prediction, and fault diagnosis, it is crucial to rapidly and accurately establish reliability modules and extract parameters from the system modules. This study employed three types of particle swarm optimization (PSO) algorithms to find the optimal parameters of two energy models by minimizing the sum squared errors (SSE) and roots mean squared errors (RMSE). The three algorithms are inertia weight PSO, constriction PSO, and momentum PSO. The obtained calculation results of these three algorithms were compared with those obtained using algorithms from other relevant studies. This study revealed that the use of momentum PSO enables rapid convergence (under 30 convergence times) and the most accurate modeling and yields the most stable parameter extraction (SSE of PEMFC is 2.0656, RMSE of PV cells is 8.839 · 10−4). In summary, momentum PSO is the algorithm that is most suitable for system parameter identification with multiple dimensions and complex modules.

Parameter Extraction of Photovoltaic Cells and Modules Using Grey Wolf Optimizer with Dimension Learning-Based Hunting Search Strategy

“…Some of other metaheuristic algorithms utilized in the literature are artificial bee swarm optimization (ABSO) [21], biogeography-based optimization with mutation strategies (BBO-M) [22], bacterial foraging algorithm (BFA) [23], bird mating optimizer (BMO) [24], grouping-based global harmony search (GGHS) [25], generalized oppositional teaching-learning-based optimization (GOTLBO) [26], improved whale optimization algorithm (IWOA) [27], pattern search (PS) [28], simulated annealing (SA) [29], self-adaptive teaching-learning-based optimization (SATLBO) [30], hybrid methods [31][32][33][34][35], etc. These metaheuristic approaches also suffer from different inherent characteristics, such as premature convergence, weak exploitation, high sensitivity to the initial population and adherence to the limited number of parameters for performance improvement [36][37][38].…”

Artificial Intelligence for Electrochemical Prediction and Optimization of Direct Carbon Fuel Cells Fueled with Biochar

“…The recent literature [14][15][16][17][18][19][20] has shown that using metaheuristic-based techniques can be so useful for solving such complex nonlinear problems. Indeed, metaheuristics are computational intelligence paradigms used especially for the sophisticated solving of optimization problems.…”

“…With minimal problem-specific knowledge required, metaheuristics are easy to implement and can be tailored to various domains. Different metaheuristics were used in the field of renewable energy such as PSO [14], artificial bee colony (ABC) [15], whale optimization algorithms (WOA) [16], genetic algorithm (GA) [17] and ant colony optimization (ACO) [18]. Azar et al [19] used the Battle Royale Optimization Algorithm (DBRA) to effectively identify the undetermined parameters in solid oxide fuel cell models.…”