Developing photovoltaic cells parameter estimation algorithm based on equilibrium optimization technique
Nowadays all the world does its best to develop the power generation systems that depend on nature in order to reduce the dependence on fuel. Photovoltaic (PV) systems are considered one of the most important renewable energy resources. Scientific research has gained a high interest, especially in PV cell modeling and parameter estimation. The estimation of optimum parameters for the PV model has been considered the main target of the paper optimization problem. Equilibrium optimization (EO) algorithm is considered one of optimization algorithms inspired from nature physical phenomena. EO algorithm has been inspired from the nature physical process of controlling mass balance through specific volume until reaching equilibrium state. In this paper, an EO algorithm has been proposed and applied to prepare a mathematical model for photovoltaic solar cell. The challenge in this optimization problem is the non-linearity in PV solar cell characteristic. The EO algorithm has been evaluated through the following items. EO has been applied to estimate the parameters of different PV models such as single, double and triple PV models, which have different complexity. Applying the previous item for real PV application. The obtained results have been compared though different functions such as root mean square value and absolute mean error. In all cases, EO obtained results have been compared with the more recent optimization algorithms such as Particle swarm optimization (PSO), Teaching learn Based Optimization (TLBO) and Harries Hawk optimization (HHO). From the all obtained results, EO algorithm gives more accurate PV models in comparison with other optimization algorithms.