| 徐耀松,包力铭,管智峰,王雨虹,阎 馨.基于 IPPA 优化 PNN 的变压器故障诊断研究[J].电子测量与仪器学报,2022,36(10):138-145 |
| 基于 IPPA 优化 PNN 的变压器故障诊断研究 |
| Research on transformer fault diagnosis based on IPPA optimization PNN |
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| DOI： |
| 中文关键词: 电力变压器 寄生捕食算法 混沌反向学习 柯西变异算子 自适应惯性权重 故障诊断 |
| 英文关键词:power transformer parasitic predation algorithm chaotic reverse learning Cauchy mutation operator adaptive inertia
weight fault diagnosis |
| 基金项目:国家自然科学基金（51974151）、辽宁省高等学校创新团队项目（LT2019007）资助 |
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| 中文摘要: |
| 针对变压器故障诊断精度低的问题,本文提出一种改进寄生捕食算法(IPPA)优化概率神经网络(PNN)的电力变压器
故障诊断模型,首先利用主成分分析(PCA)对故障数据进行数据降维减少无效特征,然后利用混沌反向学习,柯西变异算子和
融合贝塔分布的线性递减函数的权重等多策略改进寄生捕食算法( IPPA),提高其优化能力,并使用改进后的 IPPA 算法优化
PNN 网络的平滑因子,以提高 PNN 的分类精度和鲁棒性。 最后将 PCA 处理后的数据输入到 IPPA-PNN 模型中进行故障诊断
并以变压器数据为依据进行测试,测试结果表明,IPPA-PNN 模型准确率达到 93%相比于 PPA-PNN 和 PSO-PNN 模型提高了
7%和 10%能够有效地提高变压器的故障诊断性能。 |
| 英文摘要: |
| Aiming at the problem of low accuracy of transformer fault diagnosis, this paper proposes a power transformer fault diagnosis
model based on improved parasitic predation algorithm ( IPPA) and optimized probabilistic neural network (PNN). Firstly, principal
component analysis (PCA) is used to reduce the dimensionality of fault data to reduce invalid features, then use multiple strategies such
as chaotic reverse learning, Cauchy mutation operator and the weight of linear decreasing function fused with beta distribution to improve
the hunt-prey algorithm (IPPA) and its optimization ability, and use the improved IPPA algorithm to optimize the smoothing factor of the
PNN network to improve the classification accuracy and robustness of the PNN. Finally, the PCA-processed data is input into the IPPAPNN model for fault diagnosis and testing based on the transformer data. The test results show that the accuracy of the IPPA-PNN model
reaches 93%, which is 7% and 10% higher than that of the PPA-PNN and PSO-PNN models, and can effectively improve the fault
diagnosis performance of the transformer. |
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