The performance and efficiency of a solar PV cell are greatly dependent on the precise estimation of its current-voltage (I-V) characteristic. Usually, it is very difficult to estimate accurate I-V characteristics of solar PV due to the nonlinear relation between current and voltage. Metaheuristic optimization techniques, on the other hand, are very powerful tools to obtain solutions to complex non-linear problems. Hence, this chapter presents two metaheuristic algorithms, namely particle swarm optimization (PSO) and harmony search (HS), to estimate the single-diode model parameters. The feasibility of the metaheuristic algorithms is demonstrated for a solar cell and its extension to a photovoltaic solar module, and the results are compared with the numerical method, namely the Newton Raphson method (NRM), in terms of the solution accuracy, consistency, absolute maximum power error, and computation efficiency. The results show that the metaheuristic algorithms were indeed capable of obtaining higher quality solutions efficiently in the parameter estimation problem.
Integrating Blockchain Technology into Healthcare Through an Intelligent Computing Technique
