基于深度学习的智能变电站二次设备故障定位研究

Fault Location of Secondary Equipment in Smart Substation Based on Deep Learning

为提高智能变电站二次设备故障定位的准确率和运维效率,提出一种基于深度学习的智能变电站二次设备故障定位方法。依据二次设备不同模块故障时的特征信息,梳理故障定位的推理知识库;结合二次设备的自检信息、报文的订阅关系以及采样值提出了故障断面的特征信息表征方式;利用深度学习的训练规则,建立基于循环神经网络的二次设备故障定位模型并给出了故障定位步骤。以典型的智能变电站线路间隔为例,仿真验证了所提故障定位方法的有效性与精确性,且在信息可靠性不足的情况下仍能取得良好的判别结果,容错性能良好。

In order to improve the accuracy of fault location in secondary equipment in the smart substation, a fault location method for secondary equipment of the smart substation based on deep learning is proposed. According to the characteristic information of different modules in the secondary equipment, the reasoning knowledge base of the fault location is combed. Combined with the self-test information of the secondary equipment, the subscription relationship of the messages and the sampled values, the characteristic information representation of the fault section is proposed. With the training rules of deep learning, a secondary equipment fault location model based on recurrent neural network is established and the location steps are given. Taking the typical smart substation line spacing as an example, the simulation verifies the effectiveness and accuracy of the proposed fault location method. It can obtain good diagnostic results under the unreliable information, and its fault tolerance performance is good.