Condition-based Maintenance And Fault Analysis Of Batteries Using In Communication Network

Power is an important part of communication equipment. In the composition of the communication network, the power usually plays the "heart" of the role of communication. It provides electrical energy power to the AC and DC communication equipment. It is the energy of the entire communications network. Whether it can work normally or not directly affects the quality of the entire communications network. The batteries play a very important role of the communication power system and it is the final guarantee that the power system can work stably, reliably and high quality. Therefore, the study of performance and the health state of the batteries has important practical significance.This dissertation analyzes and summarize the common failures and the causes of effectiveness of VRLA batteries in order to study the conditions of their health. Firstly, summarize the common failures, according to these failures find out the reasons and the corresponding parameters of the batteries From which obtain the main parameters. These parameters are float voltage, float current,resistance and temperature. Then start from these parameters, Use the BP neural network to achieve forecasting the parameters. Establish the assessment model of the batteries state using large amount of historical data obtained to achieve the state maintenance and failure prediction. So the health state of the batteries can be predicted. Then the batteries which will become invalid or can not work normally can be timely dealed with. The aim is to reduce the communication interruption caused by power supply. It has a strong practical value to improve the reliability and security for the communication network.