基于改进PSO-BP神经网络和D-S证据理论的大型变压器故障综合诊断

阐述了已有变压器故障诊断方法的不足，并将信息融合的基本思想引入变压器故障诊断中。针对电力变压器故障综合诊断的特点和要求，运用改进粒子群优化-反向传播（PSO-BP）算法训练神经网络并结合D-S证据理论，提出了一种基于信息融合技术的变压器故障综合诊断决策模型。该模型以油中溶解气体色谱分析为基础，结合变压器常规电气试验结论与现场运行、维修经验，得出了较为可靠的诊断结果，实例验证也证明了该方法的有效性。

Power Transformer Fault Integrated Diagnosis Based on Improved PSO-BP Neural Networks and D-S Evidential Reasoning

This paper demonstrates the shortcomings of existing transformer fault diagnosis methods and introduces the basic idea of information fusion into this field. In accordance with the characteristics and requirements of power transformer fault integrated diagnosis, a new type of fault decision model based on the information fusion technique is proposed in this paper using an advanced PSO-BP (particle swarm optimization-back propagation) algorithm to train the neural network and applying the D-S evidential reasoning. By combining the dissolved gas-in-oil analysis (DGA) with the results of conventional electrical tests and on-site experience in operation and maintenance, the above model is capable of drawing credible diagnosis conclusions. The method proposed is proved effective with examples.