Abstract：For new samples that may not belong to the known discharge types in the partial discharge, a method based on variable predictive model and Tanimoto (VPM-Tanimoto) similarity is proposed to recognize the unknown types. The expression of unknown signals is achieved by constructing gradient pattern spectra, and two indicators are used to separate the unknown signals by filtering all signals in different areas. Firstly, φ -Δφ、φ -Δu、φ -Δqmax、φ -Δn and φ -n spectral patterns were built to extract features. Secondly, the corresponding VPM model group was established for each known type of discharge to predict the features for samples. Subsequently, the Tanimoto similarities between the samples and each known type of discharge were calculated to obtain the best match known category for each sample. Then, the reliability integrator discriminant analysis rule (IDAR) of the recognition results was calculated and the reliability space was divided. Different regions had different determination methods. Finally, the Tanimoto similarity and the IDAR of each region were used to double filter all discharge signals to determine and separate unknown samples. Experimental results show that the method has certain recognition effects.
邓冉, 朱永利, 刘雪纯, 翟羽佳. 基于变量预测-谷本相似度方法的局部放电中未知类型信号识别[J]. 电工技术学报, 2020, 35(14): 3105-3115.
Deng Ran, Zhu Yongli, Liu Xuechun, Zhai Yujia. Pattern Recognition of Unknown Types in Partial Discharge Signals Based on Variable Predictive Model and Tanimoto. Transactions of China Electrotechnical Society, 2020, 35(14): 3105-3115.
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