基于多策略融合粒子群算法的油纸绝缘参数辨识

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Abstract To address the issues of slow convergence speed, susceptibility to local optima, and unstable convergence results in particle swarm optimization (PSO) algorithm, this paper proposes a multi-strategy fusion particle swarm optimization (MSF-PSO) by enhancing the traditional PSO algorithm in three aspects: initial population, boundary handling, and inertia weight. Through the examination of testing functions, the MSF-PSO algorithm is proven to considerably enhance computational speed and efficiency. The MSF-PSO algorithm is applied to the identification of parameters for the Debye equivalent circuit in the dielectric response of oil-paper insulation. The computational results demonstrate that the polarization spectrum of the recovered voltage obtained by this algorithm exhibits better concordance with the polarization spectrum of the recovered voltage acquired from field tests, in comparison to other particle swarm optimization algorithms. This further validates the accuracy of the proposed method and establishes a crucial foundation for diagnosing the aging condition of transformer oil-paper insulation equipment.

Key words： oil-paper insulation parameter identification recovery voltage particle swarm optimization recovery voltage polarization spectrum

Received: 15 May 2024

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	XU Chenzhan
	LIU Qingzhen

Cite this article:

XU Chenzhan,LIU Qingzhen. Identification of oil-paper insulation parameters based on multi-strategy fusion particle swarm optimization[J]. Electrical Engineering, 2024, 25(9): 14-21.

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http://dqjs.cesmedia.cn/EN/Y2024/V25/I9/14

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