基于声纹特征和集成学习的变压器缺陷诊断方法

变压器运行过程中产生的振动噪声与其运行状态及内部缺陷情况直接相关,对其声纹信号开展特征分析,有助于进一步了解设备运行工况,保障电力系统安全稳定运行。文中以声纹特征分析为基础,兼顾诊断效率与准确性,提出一种基于卷积神经网络及集成学习模型的变压器缺陷诊断方法。该方法以变压器声纹数据的时域及频域信号为多通道输入混合特征,构建了基于卷积神经网络模型和声纹特征分析法的集成学习模型,可实现变压器声纹特征的有效识别,并通过由多个基学习器组成的集成学习模型提高了变压器缺陷诊断的准确性。基于文中所构建的变压器声纹样本库,可得到该方法对变压器单一缺陷的识别准确率为99.2%,对变压器混合缺陷的识别准确率为99.7%。研究结果表明该方法可有效识别变压器的运行状态,为变压器运维检修提供技术参考。

Transformer fault diagnosis method based on voiceprint feature and ensemble learning

The vibration and noise generated during the operation of the transformer are directly related to its operating state and internal defects. The analysis of its voiceprint characteristics is helpful to further understand the operating conditions of the equipment,and ensure the safety and stability of the power system. Based on the analysis of voiceprint features,a transformer defect diagnosis method based on deep neural network and ensemble learning model is proposed. Taking the time-domain and frequency-domain features of transformer voiceprint data as multi-channel input,an integrated learning model is constructed based on a deep neural network model,and the effective recognition of transformer voiceprint features is realized. An ensemble learning model improves the accuracy of transformer defect diagnosis. Based on the transformer voiceprint sample library constructed in this paper,the recognition accuracy rate of the method for single transformer defects is 99.2%,and the recognition accuracy rate for transformer mixed defects is 99.7%. The research results show that the method can effectively identify the operating state of the transformer,and can provide technical reference for the operation and maintenance of the transformer.