基于改进雪融优化算法的油纸绝缘扩展德拜模型参数辨识

Parameter Identification of Oil-paper Insulation Extended Debye Model Based on Improved Snow Ablation Optimizer

基于回复电压法求解变压器油纸绝缘扩展德拜模型等效电路参数,是典型的非线性多目标优化问题。为了提高扩展德拜模型参数辨识的效率和准确度,提出一种新颖的改进雪融优化(improved snow ablation optimizer,ISAO)算法,旨在有效解决扩展德拜模型参数辨识问题。ISAO算法融合了多种改进策略,运用Tent混沌映射和折射镜像学习机制提高搜索效率,引入莱维飞行策略和贪婪策略增强优化性能,并提出参数预设机制,进一步简化辨识流程、提高求解效率。将ISAO算法应用于油纸绝缘扩展德拜等效电路参数的优化求解,并与几种常用的智能优化算法进行对比,结果表明该算法在扩展德拜模型参数辨识问题上具有显著优势。

Solving the extended Debye model equivalent circuit parameters of transformer oil-paper insulation based on the recovery voltage method is a typical nonlinear multi-objective optimization problem.In order to improve the efficiency and accuracy of extended Debye model parameter identification,a novel improved snow ablation optimization algorithm(ISAO)is proposed to effectively solve the problem of extended Debye model parameter identification.The ISAO incorporates various improvement strategies,adopts Tent chaotic mapping and refractive mirror-learning mechanism to improve the search efficiency,introduces the Levy flight strategy and greedy strategy to enhance the optimization performance,and proposes the parameter presetting mechanism to further simplify the process of identification and improve the efficiency of the solution.Meanwhile,the ISAO is applied to the optimization of the parameters of the oil-paper insulated Debye equivalent circuit,and compared with several commonly used optimization algorithms for validation,which proves that the method has significant advantages in the identification of the parameters of the extended Debye model.