基于多信息融合的GIS局部放电类型识别方法研究

为了实现气体绝缘开关(gas insulated switchgear, GIS)局部放电故障类型的识别，利用GIS局部放电试验采集的特高频(ultra-high frequency, UHF)和超声波信号得到了局部放电相位解析(phase resolved partial discharge, PRPD)图和格拉姆角场(Gramian angular field, GAF)图，提出了基于自适应卷积神经网络的GIS局部放电类型识别算法，优化了卷积神经网络(convolutional neural network, CNN)超参数，建立了基于决策级多信息融合的GIS局部放电类型识别模型框架，研究了不同GIS局部放电类型识别方法的准确性及内在影响因素。结果表明：基于超高频和超声波信号的决策级多信息融合方法能够进一步提高GIS局部放电类型的识别准确性，识别准确率在98%以上，提出的决策级多信息融合方法的识别准确率高于特征级多信息融合方法，研究成果可为GIS局部放电类型识别提供技术支撑。

Research on GIS Partial Discharge Type Identification Based on Multi-Information Fusion

To enable the diagnosis of the GIS partial discharge fault type, the images of resolved partial discharge (PRPD) and gramian angular field (GAF) were obtained from the GIS partial discharge test using the ultra-high frequency method (UHF) and ultrasonic signals method. A GIS partial discharge type identification algorithm was proposed based on an adaptive convolutional neural network. The hyperparameters of the convolutional neural networks were optimized. A GIS partial discharge type identification model framework was established based on decision-level multi-information fusion. The accuracy and intrinsic influencing factors of different GIS partial discharge type identification methods were studied. The results show that the decision-level multi-information fusion method based on UHF and ultrasonic signals can further improve the identification accuracy of GIS partial discharge types, with the identification accuracy above 98%. The identification accuracy of the proposed decision-level multi-information fusion method is higher than the feature-level multi-information fusion method. The research results can provide technical support for the identification of GIS partial discharge types.