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Seyyed Mehdi Mahaei
Islamic Azad University
Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

 1 
Modeling FACTS Devices in Power System State Estimation Seyyed Mehdi Mahaei; Mehrdad Tarafdar Hagh; Kazem Zare
International Journal of Electrical and Computer Engineering (IJECE) Vol 2, No 1: February 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (286.027 KB)

Abstract

In this paper is modeled different types of control devices including various kinds of FACTS devices based on power system states. Also, the impact of each device on the amount of injection active or reactive powers as well as active and reactive power flow will be investigated. Based on the type of these devices which can be in parallel, in series or in series–shunt in power systems, proposed models is considered differently. Accordingly, case studies will be performed for three different types of control devices installed in series, in shunt and in series-shunt fashions. State estimation results based on Weighted Least Square not only confirm the proposed models’ effectiveness in accurately state estimating of the system and measurement values but also shows that the estimated values can be obtained from the states of the control devices.Keywords: State Estimation; FACTS Devises; Measurement Function; WLS EstimatorDOI:http://dx.doi.org/10.11591/ijece.v2i1.132
Improving Bad Data Detection in State Estimation of Power Systems Mehrdad Tarafdar Hagh; Seyyed Mehdi Mahaei; Kazem Zare
International Journal of Electrical and Computer Engineering (IJECE) Vol 1, No 2: December 2011
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (114.334 KB)

Abstract

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 Keywords: Bad Data; Chi-Square Distribution; Largest Normalized Residual; Hypotheses Testing Identification
Co-Authors Kazem Zare Mehrdad Tarafdar Hagh