Research on online monitoring and state diagnosis of battery for distribution automation

Abstract

Batteries have been generally adopted as energy storage component at distribution automation terminal, however bad operating environment have a great impact on the performance and service life, which is very difficult for operation and maintenance of batteries. Online monitoring and state diagnosis technology is developed, through acquisition battery real time voltage, current and temperature, use of the existing communication network of distribution automation (such as optical fiber, wireless and so on) uploaded to the battery online monitoring and state diagnosis platform; battery state diagnosis model is established using neural network based on the Unscented Kalman filter (UKF), which through battery voltage, current and temperature estimation of SOC real time value; an reasonable plan is given by online monitoring and state diagnosis platform according to SOC real time value, which provide technical basis for state-based maintenance of battery.