Optimization of Substation Alarm Information Processing Based on BP Neural Network

Abstract

When a fault occurs in the power grid, a large number of alarm messages will come out. In order to reduce the damage caused by power outages, the staff should find out the location and type of faults as soon as possible based on the alarm messages uploaded from the control center. However, we are currently facing problems in handling these alarm messages such as reliability of protection actions and circuit breaker tripping, correctness of received alarm messages, and the possibility of the existence of unreceived alarm messages. The fault tolerance of the BP neural network is studied in this paper, and the fault tolerance of the network corresponds to the size of the fuzzy zone formed by the test samples. The fault tolerance of the network is improved by eliminating the fuzzy zone, and a hierarchical causal rule reasoning network with a structure of four layers is established for each element in the station. An accessible path for the candidate cause of the fault is divided into two stages as follows: from the candidate cause to the protection operation and from the protection operation to the circuit breaker trip, which are assigned different credibility contribution rates. At the same time, the artificial bee colony algorithm is combined to identify the operation mode of the substation using its reasoning and judgment capacity and make the necessary corrections to get some output results of the neural network. The results of the examples suggest that the proposed evaluation method has a small workload of calculation and can identify the faulty components with high reliability and their causes, quickly and accurately.