Least median of squares estimation in power systems

Mili, Lamine; Phaniraj, V.; Rousseeuw, Peter J.

doi:10.1109/59.76693

Cited by 150 publications

(82 citation statements)

References 27 publications

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“…However, there are some difficulties for practical application. Repeated state and paramete estimation are needed to identify error telemetry and error network parameters, which needs a lot of calculation [7].…”

Section: A Identification Of Suspicious Branch Methods Based On Lagramentioning

confidence: 99%

Parameters Identification and Estimation in Power System Based on Lagrange Multiplier and Variable Step Integration Algorithm

Wang¹

2016

IJET

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Abstract-The parameter errors of power network can lead significantly deteriorate state estimation results in power system.In this paper, identification of suspicious parameters based on Lagrange multiplier is analyzed firstly. Then, a method of distinguishing the suspicious branch sets is proposed. Finally, the variable step integration algorithm is used to estimate the identified suspicious parameters. The proposed method has been deployed in several provincial power control centers to estimate the identified suspicious parameters. The experimental results illustrate the effectiveness of the proposed algorithm.Index Terms-Power network, parameters identification and estimation, power system, state estimation.

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Section: A Identification Of Suspicious Branch Methods Based On Lagramentioning

confidence: 99%

Parameters Identification and Estimation in Power System Based on Lagrange Multiplier and Variable Step Integration Algorithm

Wang¹

2016

IJET

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“…Milli et. al, in [14], have applied the LMS estimator on the same system. Physically, the resistance of all lines of the three-bus network are taken to be zero.…”

Section: DC Three Bus Test Systemmentioning

confidence: 99%

Robust state estimator based on maximum constraints satisfaction of uncertain measurements

Al-Othman

Irving

2007

Measurement

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`Abstract: -A new robust estimator based on the concept of uncertainty in the measurements is developed in this paper. The uncertainty in the measurements is modeled via deterministic upper and lower bounds on measurement errors, which take into account known meter accuracies. Inequality constraints are constructed to model the uncertainty in the measurements. A solution point satisfying most inequality constraints is the objective of the proposed estimator. Hence, this estimator is known as Maximum Constraints Satisfaction (MCS). The Robustness and performance of the proposed estimator is discussed via simulated problems of simple regression examples and D.C. three-bus system. Various scenarios of leverage measurements and bad data have been considered for further assessment of the performance of the MCS estimator. In particular, it is shown that the (MCS) estimator performs significantly well in situation where collinearity exists in the measurements. Results show that the proposed estimator is an accurate and reliable estimator.

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“…Chen and Abur have proposed the method of placement of PMUs to enable bad data detection in state estimation [17]. Reference [18] and [19] have devised the concepts of robust distances and influence functions in regression analysis of measurement equations to identify leverage points in a system. But they did not discuss about detecting bad data in leverage data points.…”

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confidence: 99%

“…It has been reported that there are number of ways one can identify the leverage points from the diagonal elements of the hat matrix, Mahalanobis distance (MD) of measurements, projection statistics (PS) [5], [16], etc. Reference [19] has devised the concept of influence function as a combination of influence of residuals and influence of position in factor space. Thus, looking at the influence function one can identify bad data even for influential measurements.…”

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confidence: 99%

Bad data detection in the context of leverage point attacks in modern power networks

Majumdar

2017

2017 IEEE Power &Amp; Energy Society General Meeting

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Abstract-This paper demonstrates a concept to detect bad data in state estimation when the leverage measurements are tampered with gross error. The concept is based on separating leverage measurements from non-leverage measurements by a technique called diagnostic robust generalized potential (DRGP), which also takes care of the masking or swamping effect, if any. The methodology then detects the erroneous measurements from the generalized studentized residuals (GSR). The effectiveness of the method is validated with a small illustrative example, standard IEEE 14-bus and 123-bus unbalanced network models and compared with the existing methods. The method is demonstrated to be potentially very useful to detect attacks in smart power grid targeting leverage points in the system. Index Terms-distribution management system (DMS), remote terminal unit (RTU), state estimation (SE), leverage measurements, bad data detection (BDD), generalized studentized residuals (GSR), diagnostic-robust generalized potentials (DRGP)

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Least median of squares estimation in power systems

Cited by 150 publications

References 27 publications

Parameters Identification and Estimation in Power System Based on Lagrange Multiplier and Variable Step Integration Algorithm

Parameters Identification and Estimation in Power System Based on Lagrange Multiplier and Variable Step Integration Algorithm

Robust state estimator based on maximum constraints satisfaction of uncertain measurements

Bad data detection in the context of leverage point attacks in modern power networks

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