针对单一信号源特征无法准确识别局部放电(PD)类型的问题，提出了一种基于改进BP神经网络(BPNN)和D-S证据的高压电机PD模式识别方法。对不同类型PD的脉冲相位信息、特高频信号和超声波信号进行采集，提取不同信号的特征向参数，再分别构造基于鲸鱼优化算法(WOA)改进的BPNN识别模型对PD类型识别，将3个识别模型的识别结果作为证据体采用D-S证据组合规则进行融合，最后对融合结果进行决策。研究结果表明：基于3类单一信号源独立识别各类PD类型的准确度存在差异性和不确定性，识别率分别为83.3%、90.0%、83.3%，但3类信号源的共性和差异性可以融合互补，有各自优势，可以解决故障诊断中的不确定性问题。在此基础上，基于D-S证据融合的高压电机PD类型的整体识别率提升至96.6%，实现了3种信号源的优势互补，与单一模型对比，所提方法可以稳定、准确地识别PD模式，具有更高准确率和可靠性，验证了所提方法的有效性与正确性。

The single signal source feature cannot accurately identify the type of partial discharge. In order to solve this problem, a partial discharge pattern recognition method for high-voltage motors based on improved BP neural network and D-S evidence is proposed. The pulse phase information, ultra-high frequency signal and ultrasonic signal of different types of partial discharges are collected, and the eigen parameters of different signals are extracted. Then, the improved back propagation neural network (BPNN) recognition model based on whale optimization algorithm (WOA) is constructed to identify the partial discharge types, respectively. The recognition results of each recognition model are used as the evidence body to be fused using the D-S evidence combination rule. Finally, the fusion result is decided. The research results show that there are differences and uncertainties in the accuracy of independently identifying various types of partial discharges based on three types of single signal sources, and the recognition rates are 83.3%, 90.0%, and 83.3%, respectively. However, the commonality and difference of the three types of signal sources can be integrated and complementary, and have their own advantages, which can solve the problem of uncertainty in fault diagnosis. On this basis, the overall recognition rate of high-voltage motor partial discharge types based on D-S evidence fusion has increased to 96.6%, and three signal sources have been realized. Compared with a single model, the proposed method can identify partial discharge patterns stably and accurately, and has higher accuracy and reliability, which verifies the effectiveness and correctness of the proposed method.