王亚婧, 刘 禾, 兰立刚. 基于灰色聚类和支持向量机的火电厂安全评价[J]. 现代电力, 2010, 27(3): 61-65.
引用本文: 王亚婧, 刘 禾, 兰立刚. 基于灰色聚类和支持向量机的火电厂安全评价[J]. 现代电力, 2010, 27(3): 61-65.
Wang Yajing, Liu He, Lan Ligang. Security Evaluation of Thermal Power Plant Based on Grey Clustering and Support Vector Machine Method[J]. Modern Electric Power, 2010, 27(3): 61-65.
Citation: Wang Yajing, Liu He, Lan Ligang. Security Evaluation of Thermal Power Plant Based on Grey Clustering and Support Vector Machine Method[J]. Modern Electric Power, 2010, 27(3): 61-65.

基于灰色聚类和支持向量机的火电厂安全评价

Security Evaluation of Thermal Power Plant Based on Grey Clustering and Support Vector Machine Method

  • 摘要: 针对火电厂安全评价问题, 提出了一种基于样本分类的安全评价方法。提出了表征火电厂安全性的特征空间, 根据德尔菲法得到火电厂安全评价的特征向量值。采用改进的三角白化权函数灰色聚类方法对特征向量进行聚类, 得到火电厂安全评价的样本数据。采用支持向量机对样本数据进行训练, 得到安全判别函数, 实现火电厂安全等级的模式识别。结果表明该方法能对火电厂的安全等级进行有效分类。

     

    Abstract: As to security evaluation problem of thermal power plant, a kind of security evaluation method based on sample classification is proposed. Feature space that represents security of thermal power plant is presented, and feature vector of security evaluation is obtained by Delphi Method. Feature vector is clustered by using of improved grey clustering method of triangle whitenizated weight function, and sample data of security evaluation of thermal power plant is obtained. Then sample data is trained by using of Support Vector Machine (SVM) to obtain security judgment function, and pattern recognition of security degree of thermal power plant is realized. It can be seen from the results that this method can classify security degree of thermal power plant effectively.

     

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