基于电源特征的发电统计数据异常辨识研究

电力企业对发电情况的研究一般基于发电统计数据,但统计数据中的异常数据会影响对发电情况的正确认知。针对发电统计数据的异常辨识难点,剖析了统计数据的组成结构及相互关系,分别依据自身历史数据或同类电厂数据提出了传统电源和新能源的横向与纵向聚类方法,确立了不同类型电源月度发电统计数据的异常辨识条件,提出了面向不同电源特征的发电统计数据异常辨识方法。通过实例分析,验证了该方法的合理性与有效性。

Research on Abnormality Identification for Statistical Data of Power Generation Based on Power Characteristics

Researches on power generation of power enterprises are generally based on the statistical data. However,the correct perception of the power generation will be affected by the abnormal data in the statistical data. In this study, a data abnormality identification method for statistical data based on different power characteristics was proposed. The structures and relationships of statistical data have been analyzed aiming at identifying the abnormal data from the statistical data. To establish the anomaly identification conditions of monthly generating statistical data of different types of power sources, the horizontal and vertical clustering methods of traditional power and new energy sources have been proposed based on their own historical data or similar power plant data, respectively. The rationality and effectiveness of the method have been verified by example analysis.