基于L-M算法的火电厂实时数据神经网络预测模型研究

STUDY ON REAL-TIME DATA NEURAL NETWORK PREDICTION MODEL USED IN THERMAL POWER PLANTS BASED ON THE L-M ALGORITHM

提出了一种建立在BP神经网络上的基于Levenberg-Marquardt(简称L-M)算法的火电厂实时数据神经网络预测模型,以减少训练次数和提高训练精度。通过对某电厂300MW机组高压加热器进口温度进行训练和校核,分析了数据预处理的重要性。仿真结果表明,该模型能够获得未来时刻合理的预测结果,可用于缺失数据补充和实时数据校核,提高数据可靠性,适用于在线对未来状态的评价,为状态检修提供参考依据。

A real - time data neural netwerk prediction model used in thermal power plants established on BP neural network and based on Levenberg - Marquardt （abbrevated as L - M） algorithm has been put forward, to reduce the training times and enhance the training accuracy. Through training and checking inlet temperature of the high - pressure heater for 300 MW unit in one power plant, the importance of data preprocessing has been analysed. Results of emulation show that the reasonable prediction result at future moment can be obtained by using the said model and the model can be used for supplement of shortcoming data and for real - time data checking, to enhance the reliability of data, and being applicable to on - line evaluate the future condition, providing with reference basis for condition based repair.