| 引用本文: | 王海云,田莎莎,张再驰,陈茜,卢志刚.基于负荷预测与关联规则修正的不良数据辨识方法[J].电力系统保护与控制,2017,45(23):24-33.[点击复制] |
| WANG Haiyun,TIAN Shasha,ZHANG Zaichi,CHEN Xi,LU Zhigang.A new bad data identification method based on load forecasting and the correction of association rule[J].Power System Protection and Control,2017,45(23):24-33[点击复制] |
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| 摘要: |
| 随着电力系统的快速发展，使得电网需要对海量、异构和多态的数据进行分析与辨识。传统的不良数据辨识方法辨识效率较低，且不能够高效率利用已知的全部数据信息。为解决此问题，提出了一种基于负荷预测与关联规则修正的不良数据辨识方法。根据数据量之间的内在联系，给出了一种三维矩阵的数据存储方法。建立基于回归分析法的预测模型与基于灰色关联的相关性分析模型，分析节点注入功率与温度之间的变化关系，并采用关联规则与特殊断面修正法对预测值进行修正，进而完成对注入功率的辨识。在此基础上，再通过基尔霍夫定律与残差辨识法完成对支路潮流数据的辨识工作。最后应用实际系统的仿真算例证明了该方法能够在克服残差污染和残差淹没现象的前提下准确辨识出全部的不良数据。 |
| 关键词: 不良数据辨识 数据存储 回归分析预测模型 相关性分析建模 关联规则 |
| DOI：10.7667/PSPC161842 |
| 投稿时间：2016-11-06修订日期：2017-01-21 |
| 基金项目:国家自然科学基金(61374098);教育部高等学校博士学科点专项科研基金(20131333110017) |
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| A new bad data identification method based on load forecasting and the correction of association rule |
| WANG Haiyun,TIAN Shasha,ZHANG Zaichi,CHEN Xi,LU Zhigang |
| (State Grid Beijing Electric Power Company, Beijing 100000, China;State Grid Cangzhou Power Supply Company, Cangzhou 061000, China;Hebei Key Lab of Power Electronics for Energy Conservation and Motor Drive, Yanshan University, Qinhuangdao 066004, China) |
| Abstract: |
| With the rapid development of power system, it is necessary for the grid to analyze and identify the massive, heterogeneous, polymorphic data. The efficiency of the traditional identification method is low, and it is not able to use all known data information efficiently. In order to solve this problem, a new bad data identification method based on load forecasting and the correction of association rule is proposed. At first, a three dimensional matrix data storage method is put forward according to the intrinsic relationship between the data. The forecast model of the regression analysis and the correlation analysis model based on grey correlation are established to analyze the relationship between the node power injection and the temperature. The association rules and special profile correction method are used to modify the predicted value, and then the identification of the injection power is completed. On this basis, the identification of the branch power flower is completed according to the Kirchhoff's law and the residual error identification method. At last, the simulation results of the practical system prove that the method proposed can overcome the “residual pollute” and “residual submerge” phenomenon to identify all the bad data accurately. This work is supported by National Natural Science Foundation of China (No. 61374098) and Research Fund for the Doctoral Program of Higher Education of China (No. 20131333110017). |
| Key words: bad data identification data storage forecast model of the regression analysis correlation analysis model association rules |
