Abstract
Thermal power generation is the main force of power industry. With the development of communications technologies, more and more power plants are equipped with high-precision sensors, so operating data can be gotten in real time. Therefore, unexpected breakdown detection based on operating data has become an urgent demand under the condition of digitalization. In this paper, an abnormal characteristics capture method for thermal power units before unexpected breakdown based on correlation of operating parameters is proposed. First, the Person correlation coefficient and the Box-Cox transformation are introduced to extract the correlation of the features, and the mutual information is used to screen out the correlation features used as indictor for abnormal characteristics. Secondly, a method based on three-sigma rule and Johnson transform is presented for dynamical feature threshold calculating. At last, a case study is used to illustrate the effectiveness of the method. The results show that the dynamic threshold method can predict the breakdown earlier.
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