基于长短期记忆法的换流站阀冷系统参数预测

Parameter Prediction of Valve Cooling System in Converter Station Based on Long-short Term Memory Method

在对换流站阀冷系统的内冷水流量、进出阀温度、主泵及回水压力等参数进行采样时,时常发生数据缺失或异常的情况;同时,由于阀冷系统的上述运行参数具有时序性,对内冷水系统的入水温度等关键参数完善并进行有效预测可以更准确地评估阀冷系统冷却能力。提出了一种基于均值填补的采样值处理方法,使样本集更完善、更贴合换流站阀冷系统的实际运行情况;分析了阀冷系统运行参数的时序属性,提出一种基于主成分分析的长短期记忆网络模型的时间序列预测方法,通过对换流阀关键运行参数的预测实现对系统冷却能力的有效判断。经实例验证,所建立的预测模型的平均绝对百分比误差约为4.96%,证明了所建模型的有效性。

When sampling the internal cooling water flow,inlet and outlet valve temperature,main pump and return water pressure of the valve cooling system in the converter station,data loss or abnormality often occurs.Meanwhile,due to the timing of the above operating parameters of the valve cooling system,the accurate evaluation of the cooling capacity of the valve cooling system can be achieved by completing and predicting effectively key parameters such as the inlet water temperature of the internal cooling system.A sampling data processing method based on mean filling was proposed to make the sample set more perfect and fit the actual operation of valve cooling system in converter station.By analyzing the time-series properties of valve cooling system operation parameters,a time series prediction method based on principal component analysis and long-short term memory(PCA-LSTM)model was proposed.By predicting the key operating parameters of converter valve,the cooling capacity of the system can be effectively evaluated.The simulation shows that the average absolute percentage error of the prediction model is about 4.96%,which proves the effectiveness of the model.