Risk early warning and evaluation method for electric power SDH networks based on BP neural network algorithm

Abstract

Electric power SDH network is a comprehensive communication network, which takes advantages of SDH technology, and is a widely used technology. How to protect the network more effectively is the concern of the operators. So the main purpose of this paper is to find a way to analysis risk in advance and enhance reliability of electric power SDH network. Firstly, we set up an evaluation index system and then we design a method of risk evaluation algorithm based on the BP neural network. After that we can propose the corresponding early warning model. At last, we obtain a multi-level and multi-angle risk early warning and evaluation method for electric power SDH network. We use MATLAB to obtain simulation results in three aspects, different electric power SDH network loads, different channel pressure and in the case of load balancing. Simulation results show that this method can comprehensively analyze the risks existing in the network and give the corresponding warning, and it can also achieve better evaluation effect under different load pressures.