Iteratively reweighted least-squares state estimation through Givens Rotations

Pires, Robson Celso; Costa, Antonio Simões; Mili, Lamine

doi:10.1109/59.801941

Cited by 70 publications

(37 citation statements)

References 19 publications

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“…In addition, the detection result of the measurement at 12th time point for the measurement number 10 based on robust statistics [24,25] is shown in Figure 12, This method uses a x 2 distribution ðx 2 n;0:025 Þ 1=2 with n degrees of freedom (i.e.,n ¼ n À 1, where n is the number of the bus) as a cutoff value to flag the bad data. As can be seen from Figure 12, the magnitude of the robust distance at the measurement number 10 is about 29.4077 and is greater than the cutoff value 3.582.…”

Section: Case 1: No Bad Datamentioning

confidence: 99%

Bad data analysis in power system measurement estimation using complex artificial neural network based on the extended complex Kalman filter

Huang

Lee

Shih

et al. 2010

Int Trans Elec Energy Syst

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SUMMARYThis paper proposes a method for bad data analysis in power system measurement estimation using complex artificial neural network (CANN) based on the extended complex Kalman filter (ECKF). The proposed algorithm is better in noise immunity since the link weighting in the CANN can be automatically adjusted with trained data through the ECKF. Moreover, the CANN is quite suitable for complex training data such as complex power in a power system since its input and output performs a nonlinear mapping. Four systems including a 6-bus system, the IEEE 30-bus system, IEEE 118-bus system, and a practical system are used as examples to verify the feasibility of the ECKF-CANN approach. Results show the proposed algorithm has increased sensitivity in identifying gross measurement errors with respect to the standard ANN.

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Section: Case 1: No Bad Datamentioning

confidence: 99%

Bad data analysis in power system measurement estimation using complex artificial neural network based on the extended complex Kalman filter

Huang

Lee

Shih

et al. 2010

Int Trans Elec Energy Syst

View full text Add to dashboard Cite

show abstract

“…There are non-quadratic estimators, which were developed in [18], [8] as estimators incorporating a technique of automatic bad data suppression during an estimation process. The statistical robustness of similar non-quadratic estimators is well known in statisticians [9], [7] and now they experience a rebirth as M-estimators [2], [19], [21], [11]. Although in many cases the listed above methods perform well, they still present some drawbacks stipulated by measurements local redundancy.…”

Section: Introductionmentioning

confidence: 98%

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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“…Furthermore, as the number of buses in a system increases, the ill-conditioning of the state estimation problem becomes worse. Solution methods, such as orthogonal decomposition utilizing Givens rotations [1], [2], [9], [13], have been introduced to overcome this ill-conditioning without loss of sparsity.…”

Section: Introductionmentioning

confidence: 99%