局部放电模式识别已被确定为监测电气设备运行的标准诊断工具。智能状态识别是变压器状态识别的发展趋势，但现有的智能状态识别存在模型单一、识别精度低等缺点。为了克服这一缺点，提出了一种基于D-S证据理论的多维信息源变压器局部放电故障识别方法。首先，采用小波包分解对局部放电高频信号和超声信号进行能量特征的提取。然后，根据选取的特征集，分别建立卷积神经网络（convolutional neural networks, CNN）模型和卷积神经网络-支持向量机（CNN-SVM）模型。最后，通过D-S（dempster-shafer）证据理论对两种信号识别模型的输出结果进行有效的整合。结果表明，以所提出的小波包分解能量特征集作为输入向量，两种信号CNN-SVM模型的识别率达到了95%和81.67%，分别较CNN提升了3.33%和8.34%。D-S证据理论融合方法的整体性能优于CNN和CNN-SVM，准确度和一致性较融合之前分别提高3.33%和16.66%；验证了本文方法的有效性和可行性。

Partial discharge pattern recognition has been established as a standard diagnostic tool for monitoring the operation of electrical equipment. Intelligent state recognition is the development trend of transformer state recognition, but the existing intelligent state recognition has the disadvantages of single model and low recognition accuracy. In order to overcome this shortcoming, a multidimensional information source transformer partial discharge fault identification method based on D-S evidence theory was proposed. Firstly, wavelet packet decomposition was used to extract the energy features of high frequency partial discharge signal and ultrasonic signal. Then, according to the selected feature set, the CNN (convolutional neural networks) model and CNN-SVM (convolutional neural networks-support vector machine) model were established respectively. Finally, the D-S (dempster-shafer) evidence theory was used to effectively integrate the output results of the two signal recognition models. The results show that using the proposed wavelet packet decomposition energy feature set as input vector, the recognition rates of the two signals CNN-SVM models reach 95% and 81.67%, which are 3.33% and 8.34% higher than CNN respectively. The overall performance of D-S evidence theory fusion method is better than that of CNN and CNN-SVM, and the accuracy and consistency are improved by 3.33% and 16.66% respectively. The effectiveness and feasibility of this method are verified.