首页 > 过刊浏览>2020年第39卷第4期 >164-170. DOI:10.12158/j.2096-3203.2020.04.023
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基于预测模型的发电厂异常数据辨识方法
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国家自然科学基金资助项目(51577030)


Abnormal data analysis method of power plant data based on forecast model
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    摘要:

    发电统计数据是我国实施电力监管的重要依据，其中的发电厂用电率作为反映电厂生产效能、论证节能降耗情况的关键指标，验证其上报数据的真实性和准确性十分重要。为此，提出一种基于预测模型的发电厂异常数据辨识方法。该方法首先利用Adaboost改进非线性有源自回归模型(NARX)神经网络构建发电厂用电率预测模型，通过不断引入上报值以动态的方式对当前时刻发电厂用电率进行预测。当发电厂用电率时间序列出现突变时，残差时间序列会出现明显的增大或减小，进而利用孤立森林算法得到各残差向量组的异常分值从而辨识出异常点。最后，利用该方法对注入了虚假数据的实际发电数据进行辨识，验证了所提方法的有效性。

    Abstract:

    As an important index reflecting the production efficiency and energy saving of power plants, the authenticity and accuracy of the reported data of power plant power consumption rate are very important. Therefore, a method of abnormal data identification based on predictive model is proposed. Firstly, the NARX neural network is improved by Adaboost to build the forecasting model of the power consumption rate. Then, the present power consumption rate is forecasted in a dynamic way by continuously introducing the reported value. When the time series of the power consumption rate suddenly changes, the residual time series obviously increase or decrease. Then the outliers of each residual vector group are obtained by using the isolation forest algorithm. Finally, the method is used to identify the actual generation data injected with false data, and the effectiveness of the proposed method is verified.

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高骞,张浩天,汤奕.基于预测模型的发电厂异常数据辨识方法[J].电力工程技术,2020,39(4):164-170

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  • 收稿日期:2020-02-25
  • 最后修改日期:2020-03-19
  • 录用日期:2019-11-25
  • 在线发布日期: 2020-08-03
  • 出版日期: 2020-07-28
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