Bad data identification in power system state estimation based on measurement compensation and linear residual calculation

Slutsker, I.W.

doi:10.1109/59.32457

Cited by 50 publications

(9 citation statements)

References 13 publications

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“…For any group , the sum of the squared distances measured from each sample data to its group center can be calculated as follows: (1) where is the center of group , and is the norm operator. Then, the error measure for each group can be also computed (2) As to the central concept of GSA, it is to compare the logarithmic value of with a reference value, thus the optimal number of clusters is accordingly determined. It is also noted that the GSA adopts the logarithmic representation in its mining process since the curve of error measure can thus become more linear so that the gap quantity between successive clusters is more easily determined.…”

Section: B Frameworkmentioning

confidence: 99%

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Enhancement of power system data debugging using GSA-based data-mining technique

Huang

Lin

2002

IEEE Trans. Power Syst.

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In this paper, a gap-statistic-algorithm (GSA)-based data-mining technique is applied to enhance the data debugging in power system operations. In the proposed approach, the GSA technique is embedded into a neural network frame in anticipation of improving the detection capability of bad data. Thanks to the clustering capability exhibited by GSA in which the number of clusters can be optimally determined, the proposed approach becomes highly effective to localize the group of abnormal data. This proposed approach has been tested through the data collected from different scenarios made on an IEEE 30-bus system and 118-bus systems. Test results reveal the feasibility of the method for the data diagnosis applications.

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Section: B Frameworkmentioning

confidence: 99%

“…Then, the error measure of the clustered data is computed by (2) shown in Section II. To distinguish from the reference data discussed in the next step, is termed for the error measures of clustered input data while is termed for that of clustered reference data.…”

Section: Computation Proceduresmentioning

confidence: 99%

Enhancement of power system data debugging using GSA-based data-mining technique

Huang

Lin

2002

IEEE Trans. Power Syst.

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“…The identification by elimination based on rN fails to identify both of them since it eliminates successively measurements #4 and #5 instead. This wrong solution is found as the optimal one by the combinatorial optimization method [ll] (see Fig.5), as well as by the geometric method [12] and the method proposed in [13]. Recall that the HTI method fails as soon as all the bad data have not been selected [lo].…”

Section: Example Of a 3-bus DC Modelmentioning

confidence: 99%

Least median of squares estimation in power systems

Mili

Phaniraj

Rousseeuw

1991

IEEE Trans. Power Syst.

150

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Static state estimators currently in use in power systems are prone to masking by multiple bad data. This is mainly because the power system regression model contains many leverage points ; typically they have a cluster pattern. As reported recently in the statistical literature, only high breakdown point estimators are robust enough to cope with gross errors corrupting such a model. This paper deals with one such estimator, the least median of squares estimator, developed by Rousseeuw in 1984. The robustness of this method is assessed while applying it to power systems. Resampling methods are developed, and simulation results for IEEE test systems discussed.

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“…Some of them based on the post-filtering analysis of the measurement normalized residuals [25], [27] etc. Others are performed before a state estimation and are based on analysis of testing equations [5].…”

Section: Introductionmentioning

confidence: 99%

Constrained power system state estimation on recurrent neural networks

Хохлов

2005

2005 IEEE Russia Power Tech

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A new method for power system state estimation which combines robust M-estimation with treatment of the inequality constraints is presented. The main advantage of the method is that most expensive computation is performed in a neural network which is amenable to parallel implementation. The designed recurrent neural networks are based on differential equations and realize searching a saddle point for appropriate Lagrangian function. Test results on standard test system are used to illustrate the effectiveness of the method.Index Terms -power system state estimation, recurrent neural network, robustness.

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Bad data identification in power system state estimation based on measurement compensation and linear residual calculation

Cited by 50 publications

References 13 publications

Enhancement of power system data debugging using GSA-based data-mining technique

Enhancement of power system data debugging using GSA-based data-mining technique

Least median of squares estimation in power systems

Constrained power system state estimation on recurrent neural networks

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