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Abstract：
To investigate the pattern recognition of complex defect types in XLPE (cross-linked polyethylene) cable partial discharges and analyze the effectiveness of identifying partial discharge signal patterns, this study employs the variational mode decomposition (VMD) algorithm alongside entropy theories such as power spectrum entropy, fuzzy entropy, and permutation entropy for feature extraction from partial discharge signals of composite insulation defects. The mean power spectrum entropy (PS), mean fuzzy entropy (FU), mean permutation entropy (PE), as well as the permutation entropy values of IMF2 and IMF13 (Pe) are selected as the characteristic quantities for four categories of partial discharge signals associated with composite defects. Six hundred samples are selected from the partial discharge signals of each type of compound defect, amounting to a total of 2400 samples for the four types of compound defects combined. Each sample comprises five feature values, which are compiled into a dataset. A Snake Optimization Algorithm-optimized Support Vector Machine (SO-SVM) model is designed and trained, using the extracted features from cable partial discharge datasets as case examples for recognizing cable partial discharge signals. The identification outcomes from the SO-SVM model are then compared with those from conventional learning models. The results demonstrate that for partial discharge signals of XLPE cable composite insulation defects, the SO-SVM model yields better identification results than traditional learning models. In terms of recognition accuracy, for scratch and water ingress defects, SO-SVM improves by 14.00% over BP (Back Propagation) neural networks, by 5.66% over GA-BP (Genetic Algorithm-Back Propagation), and by 12.50% over SVM (support vector machine). For defects involving metal impurities and scratches, SO-SVM improves by 13.39% over BP, 9.34% over GA-BP, and 12.56% over SVM. For defects with metal impurities and water ingress, SO-SVM shows enhancements of 13.80% over BP, 9.47% over GA-BP, and 13.97% over SVM. Lastly, for defects combining metal impurities, water ingress, and scratches, SO-SVM registers increases of 11.90% over BP, 9.59% over GA-BP, and 12.05% over SVM.
摘要：
研究交联聚乙烯（XLPE）电缆局部放电中复杂缺陷类型的模式识别，并分析识别局部放电信号模式的有效性，这项研究采用了变分模式分解(VMD)算法和熵理论，如功率谱熵，模糊熵,和排列熵对复合绝缘缺陷局部放电信号进行特征提取。平均功率谱熵(PS)，平均模糊熵(FU)，平均排列熵(PE)，以及IMF2和IMF13（Pe）的排列熵值被选择为与复合缺陷相关的四类局部放电信号的特征量。从每种复合缺陷的局部放电信号中选取600个样本,共2400个样品的四种类型的复合缺陷的组合。每个样本包含五个特征值，它们被编译成数据集。设计并训练了Snake优化算法-优化支持向量机(SO-SVM)模型,使用从电缆局部放电数据集中提取的特征作为识别电缆局部放电信号的案例示例。然后将SO-SVM模型的识别结果与常规学习模型的识别结果进行比较。结果表明,对于交联聚乙烯复合绝缘缺陷电缆的局部放电信号,SO-SVM模型比传统学习模型具有更好的识别效果。在识别精度方面，对于划痕和进水缺陷，SO-SVM比BP(反向传播)神经网络提高了14.00%，比GA-BP(遗传算法-反向传播)高出5.66%，比SVM(支持向量机)高出12.50%。对于涉及金属杂质和划痕的缺陷，SO-SVM比BP提高了13.39%，比GA-BP高出9.34%，和12.56%的SVM。对于有金属杂质和进水的缺陷，SO-SVM比BP增强了13.80%，比GA-BP高出9.47%，和13.97%的SVM。最后,对于结合金属杂质的缺陷，水进入,和划痕，SO-SVM寄存器比BP增加了11.90%，比GA-BP高出9.59%，比SVM高出12.05%。
