Data mining based plant-level load dispatching strategy for the coal-fired power plant coal-saving: A case study

被引：30
作者：
Xu, Jing [1 ]
Gu, Yujiong [1 ]
Chen, Dongchao [1 ]
Li, Qianqian [1 ]
机构：
[1] North China Elect Power Univ, Natl Thermal Power Engn & Technol Res Ctr, Beinong St, Beijing 102206, Peoples R China
来源：
APPLIED THERMAL ENGINEERING | 2017年 / 119卷
关键词：
Coal-fired power plant; Load dispatching; Coal rate; Support vector machine;
D O I：
10.1016/j.applthermaleng.2017.03.094
中图分类号：
O414.1 [热力学];
学科分类号：
摘要：
One of the most urgent problems facing coal-fired power plants in China today is the coal-waste because several units in one plant experience a partial rated output situation at the same time, which may increase the coal consumption of the power plant. Here we proposed a new hybrid methodology for plant-level load dispatching to minimize coal consumption for the coal-fired power plant. The proposed strategy consists of two parts: A part was to establish the coal consumption model to find the relationship between the coal rate, ambient temperature and load based on PSO-SVM; the other one based on GA to find the load-dispatching optimal solution to minimize the average coal rate of the whole power plant. Additionally, grey correlation analysis was employed to pick up the most relevant input variables to reduce the complexity of the regression model. This work is based on continuously measured supervision information system data from an actual coal-fired power plant with three different types of capacity units in China. Results showed that the proposed strategy performances better than the normal AGC to single unit for power plant energy saving. It is of significant for coal-fired power plant to reduce the coal consumption. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码：553 / 559
页数：7
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