Comprehensive evaluation method for thermal power units based on principal component analysis and cluster analysis

WANG Shoupeng; ZHAO Dongmei; SHANG Liqun; CHEN Chi

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Comprehensive evaluation method for thermal power units based on principal component analysis and cluster analysis

Thermal Power Generation Vol. 46, Issue 5, Pages: 74-79(2017)

更新时间：2025-09-28

- Comprehensive evaluation method for thermal power units based on principal component analysis and cluster analysis
- 作者机构：
  
  1. 华北电力大学电气与电子工程学院
  2. 西安科技大学电气与控制工程学院
  3. 华能应城热电有限公司
- 作者简介：
- 基金信息：
- DOI：
  CLC： TM621
- Published：2017
- 稿件说明：
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WANG Shoupeng, ZHAO Dongmei, SHANG Liqun, et al. Comprehensive evaluation method for thermal power units based on principal component analysis and cluster analysis[J]. Thermal Power Generation, 2017, 46(5): 74-79.
DOI：

WANG Shoupeng, ZHAO Dongmei, SHANG Liqun, et al. Comprehensive evaluation method for thermal power units based on principal component analysis and cluster analysis[J]. Thermal Power Generation, 2017, 46(5): 74-79. DOI：

摘要

针对火电机组运行状态评价中涉及到的多指标体系的综合评价问题

本文将主成分分析和聚类分析相结合应用于机组运行状况综合评价。首先运用主成分分析法对单一指标评价体系进行综合

抽取影响火电机组运行状况的主要因素;然后以提取出的主成分作为新的数据矩阵进行聚类分类

避免了经典聚类定性选择聚类变量的主观性;最后利用主成分得分的大小来量化各因素的优劣性

评价结果全面地反映了机组的整体运行状况。实例分析结果验证了本文所提方法的有效性。

Abstract

In order to solve the problem of comprehensive evaluation for multi-indicator system involved in operation state evaluation of thermal power units

the principal component analysis and cluster analysis are combined and applied to the comprehensive evaluation of power units. This method utilizes the principal component analysis to perform dimension-reduction

standardization and decorrelation for unit operation indicators

then the principal components affecting the unit operation state are extracted. Moreover

the extracted principal components are considered as a new data matrix for cluster analysis

which avoids the disadvantage of subjectivity of the classical clustering method when choosing the clustering variables. Finally

the principal component scores and the cluster classification are combined for the comprehensive evaluation. The case study result verified the effectiveness of the above method.`

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