期刊名称：International Journal of Electrical and Computer Engineering
电子版ISSN：2088-8708
出版年度：2011
卷号：1
期号：2
页码：85-92
语种：English
出版社：Institute of Advanced Engineering and Science (IAES)
摘要：In state estimation of power systems, it is possible that measurements include bad data, influencing on state estimations of power system. Several intelligent methods have been proposed to detect bad data which should be trained in various network situations but they are almost impractical because of abound situations of actual network. Some mathematical methods such as Chi-Square Distribution Test, Largest Normalized Residual Test and Hypotheses Testing Identification as the detectors of bad data have been presented, too. Sometimes these mathematical methods are not able to detect bad data. This paper proposes a method which can improve the detection of bad data in mentioned mathematical methods. Case studies have been done with different given errors on measurements of IEEE 14-bus system, and it was shown that this method is effective to improve the bad data detection. DOI: http://dx.doi.org/10.11591/ijece.v1i2.133
其他摘要：In state estimation of power systems, it is possible that measurements include bad data, influencing on state estimations of power system. Several intelligent methods have been proposed to detect bad data which should be trained in various network situations but they are almost impractical because of abound situations of actual network. Some mathematical methods such as Chi-Square Distribution Test, Largest Normalized Residual Test and Hypotheses Testing Identification as the detectors of bad data have been presented, too. Sometimes these mathematical methods are not able to detect bad data. This paper proposes a method which can improve the detection of bad data in mentioned mathematical methods. Case studies have been done with different given errors on measurements of IEEE 14-bus system, and it was shown that this method is effective to improve the bad data detection. DOI:
关键词：Bad Data; Chi-Square Distribution; Largest Normalized Residual; Hypotheses Testing Identification
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