首页|联合循环发电站电力输出预测

联合循环发电站电力输出预测

Electrical Power Output Prediction of Combined Cycle Power Station

扫码查看
原文链接
  • 维普
  • 万方数据
为了使联合循环发电站利润最大化,准确预测其满负载电力输出非常重要.联合循环发电站运行时,前一级产生的废气被用来驱动下一级热机,以此来推动发电机,其满负载电力输出受到环境温度、大气压强、相对湿度和废气气压的影响.为此,首先,采用核主成分分析(kernel principle component analysis,KPCA)对电站发电相关的特征进行特征组合降维完成特征提取;然后,采用极端梯度提升(extreme gradient boosting,XGBoost)算法进行特征重要性评分,并结合序列前向选择法(forward selection,FS)获取最优特征子集;最后,构建了KPCA-XGB-FS模型用于联合循环发电站满负载下小时电力输出预测.通过对某联合发电站的真实数据进行实验,并与使用相同数据的已有研究方法进行对比,结果表明,所提出方法能够有效对电力输出进行预测,预测结果优于已有的研究方法.
In order to maximize the profit of a combined cycle power plant,it is important to accurately predict its full load power output.When a combined cycle power plant operates,the exhaust gas produced by the previous stage is used to drive the next stage heat engine to drive the generator,and its full-load electrical output is affected by ambient temperature,atmospheric pressure,relative humidity and exhaust pressure.Firstly,the kernel principle component analysis(KPCA)was used to perform feature combination dimensionality reduction for power generation related features to complete feature extraction.Then,the extreme gradient boosting algorithm(XGBoost)was used to score feature importance and the optimal feature subset was obtained by forward selection(FS).Finally,a KPCA-XGB-FS model was constructed for the prediction of hourly power output under full load of combined cycle power plants.By experimenting with real data from a combined power station and comparing with existing research methods using the same data,it is found that the method proposed in this paper can effectively predict the power output,and is better than the existing research methods.

combined cycle power plantelectrical power outputfeature extractionkernel principle component analysis(KPCA)forward selection

陈代俊、陈里里、李阳涛

展开 >

重庆交通大学机电与车辆工程学院,重庆市 南岸区 400074

联合循环发电站 电力输出 特征提取 核主成分分析(KPCA) 前向选择

重庆市技术创新与应用发展专项重点项目城市轨道交通车辆系统集成与控制重庆市重点实验室开放课题

cstc2020jscxgksbX0010CKLURTSIC-KFKT-202006

2024

10.12096/j.2096-4528.pgt.22046

发电技术
华电电力科学研究院

发电技术

CSTPCD
影响因子：0.388
ISSN：2096-4528
年,卷(期)：2024.45(1)
  • 18