Dynamic Model Construction of Surge Arrester Based on W-MOPSO Algorithm

Metal oxide surge arresters are of great significance for surge arrester configuration, insulation coordination research, and system reliability calculation. Their accurate modeling depends on precise parameter calculation, and model parameter optimization is a complex multi-objective optimization problem. Therefore, this paper proposes a multi-objective particle swarm optimization method based on the Weighted Multi-Objective Particle Swarm Optimization (W-MOPSO) algorithm to optimize the parameters of the surge arrester dynamic model. This allows the surge arrester dynamic model to be applicable to steep lightning current simulation, lightning current simulation, and switching current transient simulation simultaneously. The W-MOPSO algorithm performs Pareto dominance ranking on the global optimal solution of the weighted sum of multiple objective functions to obtain a Pareto optimal solution set. Finally, the model construction and parameter optimization analysis are performed using the YH5WZ-17/45 surge arrester as an example. By comparing the predicted residual voltage with the experimental results and comparing the optimization effects of four different algorithms, the surge arrester dynamic model optimized by the proposed algorithm can make the absolute value of the relative average error of multiple optimization objectives less than 4<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup>/0, effectively promoting the optimization of dynamic surge arrester modeling parameters and ensuring the accuracy of the model.