A new stochastic slime mould optimization algorithm for the estimation of solar photovoltaic cell parameters

With the help of accurate solar photovoltaic (PV) cell modeling, the PV system's performance can be enhanced. However, PV cell modeling is erroneously caused by inaccurate solar cell parameters. In general, the manufacturers will not provide the required data to model PV cells accurately. Thus, it is essential to get the PV cell parameters effectively. With this primary motivation, this paper presents a new stochastic optimization algorithm for estimating the solar PV cell parameters. Numerous optimization algorithms are discussed in the literature, and nevertheless, due to the convergence towards local minima, the sub-optimal results are produced by most of the algorithms. Thus, in this paper, a new algorithm named as Slime Mould Algorithm (SMA) is presented for the solar cell estimation. The proposed algorithm has a new feature called as an exceptional mathematical model with adaptive weights to simulate negative and positive feedback of the propagation wave to find the best path for attaching food with an excellent exploitation tendency and exploratory capacity. The performance of the proposed SMA algorithm is validated by comparing the estimated results with experimental results. The superiority of the SMA algorithm is proved by extensive statistical analysis. In addition, the performance of the proposed algorithm is also compared with the other benchmark meta-heuristics algorithms.