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Identifying the equivalent network parameters of the transformer winding is essential for the interpretation of the frequency response analysis (FRA) and driving-point admittance (DPA) data. This chapter presents a method for parameter identification that uses the DPA data and the improved whale optimization algorithm (IWOA) to invert the equivalent network parameters of the transformer. First, the DPA measurement model and its state-space equation of a double-winding transformer were established. Next, an objective function was constructed by the resonant amplitudes of the reference DPA curve and that of the estimation DPA derived by the above-mentioned state space equation. Then, an improved whale algorithm was proposed by updating the population generation method, the convergence factor, and the inertia weight of the whale algorithm. Finally, the validity and advantage of the parameter recognition method, based on the objective function and the IWOA, were proved by identifying the parameters of the simulation model and the comparison with the particle swarm optimization (PSO) and genetic algorithm (GA) algorithms.

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Parameter Identification for Transformer Winding Equivalent Networks Based on an Improved Whale Optimization Algorithm

  • 会议论文

  • 发表日期：

    2025年

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  • Yong Liu,
  • Xiaobo Pei,
  • Yuye Gong,
  • Qinggong Qi,
  • Yongsheng Zhu,
  • Xiaolei Wang

摘要

Identifying the equivalent network parameters of the transformer winding is essential for the interpretation of the frequency response analysis (FRA) and driving-point admittance (DPA) data. This chapter presents a method for parameter identification that uses the DPA data and the improved whale optimization algorithm (IWOA) to invert the equivalent network parameters of the transformer. First, the DPA measurement model and its state-space equation of a double-winding transformer were established. Next, an objective function was constructed by the resonant amplitudes of the reference DPA curve and that of the estimation DPA derived by the above-mentioned state space equation. Then, an improved whale algorithm was proposed by updating the population generation method, the convergence factor, and the inertia weight of the whale algorithm. Finally, the validity and advantage of the parameter recognition method, based on the objective function and the IWOA, were proved by identifying the parameters of the simulation model and the comparison with the particle swarm optimization (PSO) and genetic algorithm (GA) algorithms.