【作者】 薛淑香；

【导师】 顾煜炯；

【作者基本信息】 华北电力大学（北京） ， 热能动力工程， 2005， 硕士

【摘要】 近年来,组合预测在许多领域中的成功应用,为发电设备的状态预测技术的发展开拓了新的途径。针对发电设备系统复杂、难以进行精确状态预测的问题,在系统状态特征参数提取的基础上,本文提出了基于灰色 GM(1,1)模型和 BP 神经网络的组合状态预测模型。该模型充分利用设备运行参数、状态监测参数和运行统计参数,实现了发电设备的状态预测。 给水系统是火力发电厂热力循环的一个重要组成部分,开展给水系统状态预测的研究具有重要的现实意义。因此,本文将此组合状态预测技术应用于给水系统中。结果表明,该模型具有较高的预测效率和精度,为下一步的维修决策提供科学依据。更多还原

【Abstract】 In recent years, the successful using of combining prediction method in manyfields has exploited new way for the development of condition forecasting technology ofpower generating equipment.In this paper, for solving the problem that the equipment inpower plant are complex and difficult to predict their conditions accurately, a model ofcombining condition prediction on equipment in power plant based on grey GM(1,1)model and BP neural network is proposed on the basis of characteristic conditionparameters extraction. By fully using the operating parameters, condition monitoringparameters and operation statistic parameters, the conditions of equipment are predicted. The feed water system of boiler is one of important system of thermodynamiccirculation in power plant. It has important real signification to study the conditionforecasting of feed water system. Therefore, this paper applied the study fruit ofcombining condition prediction on the feed water system in power plant. The resultsreveal that the model has high efficiency and precision. The predicted results can be usedas a support next in making scientific maintenance decisions.更多还原