基于知识学习的储能电站健康监测与预警

Health monitoring and early warning of an energy storage plant based on knowledge learning

针对储能电站系统在运行状态的单体健康问题,提出一种基于核密度估计和长短期记忆(Long Short-Term Memory,LSTM)模型的健康监测和预警系统。该系统由两部分构成,一部分是基于核密度估计的健康度评估模块,该模块可以对储能电站内部所有的储能电池进行周期性巡检。基于BMS采集的历史数据,利用核密度估计方法及时检查电池单体的健康状态。另一部分是基于LSTM模型的故障预警模块,LSTM可以对监测变量在较长时间内的动态变化做出预测,结合核密度估计出的控制限,从而做出故障预警。实验结果表明:通过对实际电网运行历史数据的分析,验证了上述方法的有效性和实用性,证明了其可以有效降低因控制限不准确引起的故障漏报或误报率。

A health monitoring and fault early warning system based on a Long Short-Term Memory(LSTM)model and nuclear density estimation is proposed.The system is composed of two parts.The first part is the health assessment module based on nuclear density estimation.This can periodically inspect all energy storage batteries in an energy storage station.Based on the historical data collected by BMS,the health status of battery cells can be checked in good time using the nuclear density estimation method.The other part is the fault early warning module based on the LSTM model.LSTM can predict dynamic changes of monitoring variables over a long period,combined with the control limit estimated by the nuclear density,so as to give early fault warning.The experimental results show that by analyzing the historical data of the actual power grid operation,the effectiveness and practicability of the above method are verified.It is proved that the method can effectively reduce the rate of missed faults or false alarms caused by an inaccurate control limit.