基于互信息参数优化BP神经网络的生物质发电量预测研究  

Research on biomass power generation prediction based on mutual information parameter optimization and BP neural network

作　　者：佟敏 史昌明 马善为 崔亚茹 李凯[2] Tong Min;Shi Changming;Ma Shanwei;Cui Yaru;Li Kai(Electric Power Research Institute of State Grid East Inner Mongolia Electric Power Co.,Ltd.,Hohhot,010020,China;National Engineering Research Center of New Energy Power Generation,North China Electric Power University,Beijing,102206,China)

机构地区：[1]国网内蒙古东部电力有限公司电力科学研究院,呼和浩特市010020 [2]华北电力大学新能源发电国家工程研究中心,北京市102206

出　　处：《中国农机化学报》2023年第2期126-131,共6页Journal of Chinese Agricultural Mechanization

基　　金：国家电网有限公司总部管理科技项目资助(5400—202031205A—0—0—00)。

摘　　要：生物质直燃发电是目前应用最广、规模最大的生物质能利用方式。然而由于生物质种类繁多、理化性质多变、燃烧不稳定,使得发电量难以准确预计,这为电网调度、安全运行带来隐患。为此,提出一种基于互信息参数优化BP神经网络的生物质发电量预测模型。从生物质电厂收集发电量以及物料参数、锅炉参数、汽机参数、环境参数等实际生产数据,采用平均影响值分析、相关分析和互信息分析对发电量的影响因素进行优化选择,并利用电厂实际数据建立BP神经网络模型。测试结果表明,采用优化影响因素建立的神经网络模型预测误差大幅度降低,其中互信息分析优化效果最佳,平均预测误差从未优化模型的4.59%降至0.66%,且进一步优化神经网络参数后,平均预测误差降至0.50%。Biomass direct combustion power generation is the most widely and largest biomass energy utilization technology at present. It has various advantages such as simplicity, high efficiency, economics, etc. However, due to the wide variety, changeable physicochemical properties and unstable combustion of biomass, it is difficult to accurately predict the power generation, which brings hidden dangers to power grid dispatching and safe operation. Based on this, this study proposes a BP neural network biomass power generation prediction model based on mutual information parameter optimization. Firstly, the actual power production data are collected from a biomass power plant, including power generation, material parameters, boiler parameters, steam turbine parameters, flue gas parameters, etc. Then, the influencing factors of power generation are optimized and filtered by using average influence value analysis, correlation analysis and mutual information analysis. Finally, the whole collected data of power plant are used to establish BP neural network models. The test results show that the relative error of the neural network model established by optimizing data is greatly reduced, the mutual information analysis shows the best optimization effect, and the corresponding average prediction error reduces from 4.59% to 0.66%. Further, the parameters of the neural network model is optimized, and the average prediction error can reduce to 0.50%.

关 键 词：生物质 发电量 互信息 参数优化 BP神经网络 

分 类 号：TM61[电气工程—电力系统及自动化]