基于模糊环境AMPSO-SVM氧化锌避雷器故障诊断研究

Study on Fault Diagnosis for Zinc Oxide Arrester Based on Hybridfuzzy Environment AMPSO-SVM

阐述了氧化锌避雷器在线监测系统中故障诊断的常规方法与不足,对此提出一种基于模糊环境AMPSO-SVM的氧化锌避雷器故障诊断方法。首先将影响避雷器泄露电流的周围环境部分模糊化,以此作为支持向量机(SVM)的训练样本,在此基础上通过运用自适应变异粒子群算法(AMPSO)对支持向量机中的惩罚因子C和核函数参数g寻优,以得到最佳的诊断模型。并将所得模型和BP神经网络的故障诊断模型相比较,MATLAB仿真结果表明基于模糊环境AMPSO-SVM的诊断方法有更高的正确率和泛化能力,能够较准确的判断复杂环境下氧化锌避雷器的运行状况。

This work elaborates the conventional fault diagnosis application of Zinc Oxide surge atrester for online monitoring system and its shortcomings. Subsequently, an improved Zinc Oxide arrester fault diagnosis method based on fuzzy environment AMPSO-SVM is proposed. Firstly, the surrounding en- vironment is fuzzified that affects the leakage current of arrester, which could be taken as the support vector machine（SVM） training samples. Then, the optimized values of SVM penalty factor C and kernel function parameters g are acquired by the adaptive mutation particle swarm optimization algorithm （AMPSO） to obtain the optimum model. After that, this new model is compared with the fault diagnosis model of BP neural network and the MATLAB simulation results show that the AMPSO-SVM diagnostic method has higher accuracy and generalization ability, which thus can make a more accurate determine on the status oiZinc Oxide lightning arrester under complex working environment.