Research Of Substation Equipment On-line Monitoring And Fault Diagnosis System

The safe operation of the power system influences the national economic development and the quality of national life directly. In order to ensure the safe operation of the substation, the State Grid puts forward higher requirements to equipment in substation, especially primary equipment. Among them, the wireless sensor network with advantages of no wiring, robust and easy to extend can be regarded as an important way of monitoring substation equipment. The transformer has a pivotal role in substation,and it is also one of the core equipments in power grid, the safe and stable operation of transformer is directly related to the safety of electric power system. Transformer is one of the electric equipment that accident frequently appears, transformer malfunction will not only caused huge property damage, but also directly affect the normal life of people. In view of the importance of the transformer,how to analyse transformer working state and diagnose transformer faults has become the focus of electric power staff.First,this paper introduces the concrete contents of equipment monitoring in substation, and analyzes the application of wireless sensor networks in on-line monitoring in substation, and puts forward the whole design scheme. Aiming at the energy limited problem of wireless sensor nodes, and proposes an improved scheme on wireless sensor network MAC layer protocol of S-MAC, in order to reduce the energy consumption of wireless sensor nodes.Then this paper introduces traditional methods of transformer diagnosis, and analyzes some disadvantages exist, such as the classification of fuzzy, inaccurate judgment. Through the introduction contents and characteristics of radial basis function(RBF) neural networks and quantum computing, analyzes their advantages in fault diagnosis of power transformer. Propose to use the quantum RBF neural network algorithm as the transformer fault diagnosis algorithm that can make the efficiency of transformer fault diagnosis and accuracy are improved greatly.Training the quantum RBF neural network by using the sample data of transformer,and select test data to examine the performance of quantum RBF neural network.The results show that the algorithm can make the efficiency and accuracy of transformer fault diagnosis greatly enhanced.Finally, in order to achieve target that a comprehensive monitoring of substation equipment,design and implement substation equipment online monitoring and diagnosis system based on WEB. The system can realize comprehensive monitoring of key equipment including transformers in substation, diagnosis equipment according to the real-time status data and analyze the causes of equipment failure. The system can better achieve information integration construction in substation.