基于遥信数据故障编码技术及DHNN校正的电网故障诊断方法

提出采用故障编码技术形成故障空间最优编码集,然后通过模板匹配的方式进行电网故障诊断的方法。针对由于缺少前端故障遥信数据处理的清洗算法,造成故障诊断算法诊断正确率不高的问题,提出了建立离散Hopfield神经网络模型用于故障遥信数据的前端数据清洗的算法。利用故障遥信数据之间的相关性对遥信变位数据进行分组,并对各组数据分别采用所提出的算法进行数据清洗,利用穷举输入状态数据的方法求取了算法的修正域,从而建立了DHNN清洗模型。最终形成具有纠错能力的电网故障智能诊断方法,实现在故障诊断空间内对故障元件的诊断。通过实际电网的故障遥信数据的测试,验证了DHNN神经网络信息纠正模型和故障诊断模型对电网故障元件诊断的有效性。

A fault diagnosis method of power grid based onremote signal data fault coding technology and DHNN correction

This paper proposes a method for power grid fault diagnosis based on fault coding technology, which first forms the optimal code set of fault space and then makes template matching. Aiming at the problem that the diagnostic accuracy of fault diagnosis algorithm is not high due to the lack of cleaning algorithms for front-end fault remote signal data processing, this paper proposes an algorithm of establishing discrete Hopfield neural network model for front-end data cleaning of fault remote signal data. The correlation between the faulted remote signal data is used to group the remote signal displacement data, and the algorithm proposed in this paper is used for data cleaning, and the modified domain of the algorithm is obtained by exhaustive input state data. Thus the DHNN cleaning model is established. Finally, an intelligent power grid fault diagnosis method with error correction capability is formed to realize the diagnosis of fault elements in the fault diagnosis space. The validity of Hopfield neural network information correction model and fault diagnosis model in fault component diagnosis of power grid is verified by testing the fault remote communication data of actual power grid. This work is supported by National Natural Science Foundation of China (No. 51777119) and Science and Technology Project of State Grid Shanghai Electric Power Company (No. 520900170024).