Fault Identification of Secondary Equipment in the Intelligent Substation

Abstract

Abstract With the continuous expansion of the scale of the power system and the increasingly complex structure, the intelligent construction of the power grid continues to deepen and the construction scale of smart substations as the core part of the smart grid is also expanding. At present, higher requirements are put forward for the operation and maintenance of the secondary system of the smart substation. Therefore, the fault diagnosis of the secondary system of smart substations has become an important research direction for smart grid construction. This paper proposes a secondary system fault location method for smart substations. The method uses a search algorithm based on an adjacency list and skyline query to determine the fault area. This scheme is applicable when the fault information is complete or not. Compared with the method of using the neural network to directly locate the fault of the whole network components, the training scale of the neural network is reduced and the “uncritical” feature quantity is reduced for the fault location. It highlights the value of “key” feature quantities and effectively improves the accuracy of the fault location.