Recent experience with a hybrid SCADA/PMU on-line state estimator

Avila-Rosales, R.; Rice, Mark; Giri, J.; Beard, Lisa; Galvan, Floyd

doi:10.1109/pes.2009.5275954

Cited by 26 publications

(16 citation statements)

References 3 publications

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“…In the second, a two stage SE is used, where the results of the first stage from a conventional SE and the phasor measurements are used in the second stage to form a linear SE. Inclusion of phasor measurements within static SE has been reported by several utilities [16][17][18][19]. The penetration of PMU measurements compared to conventional measurements is, however, still too limited to have a noticeable impact on the SE solution [20].…”

Section: Introductionmentioning

confidence: 99%

State estimation and bad data processing for systems including PMU and SCADA measurements

Korres

Manousakis

2011

Electric Power Systems Research

146

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Section: Introductionmentioning

confidence: 99%

State estimation and bad data processing for systems including PMU and SCADA measurements

Korres

Manousakis

2011

Electric Power Systems Research

146

View full text Add to dashboard Cite

“…. , n in (4) and (5) is known, but all other parameters are unknown, namely M i , E i , d i , r ik , and x ik . An example of such a network consisting of n = 6 generators and m = 9 tie-lines is shown in Fig.…”

Section: Problem Formulationmentioning

confidence: 99%

Optimal Measurement Allocation Algorithms for Parametric Model Identification of Power Systems

Chakrabortty

Martin

2014

IEEE Trans. Contr. Syst. Technol.

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In this paper, we present an optimization algorithm that selects the optimal sets of points for placing phasor measurement units (PMUs) on the transmission lines of a multimachine power system for the purpose of identifying the best model fit for its wide-area swing dynamics. Alternatively, the method can also be viewed as a way to select the optimal set of points at which phasor values should be computed using measurements available from PMUs such that these computed values, also referred to here as pseudo-measurements, can generate the best estimate of the swing model, especially when the measurements are noisy. We pose the identification problem as an equivalent parameter estimation problem for the admittance of each tieline and the inertia of each machine using phasor measurements of voltage magnitude, phase angle and frequency, corrupted with high-frequency measurement noise. We then formulate the Cramer-Rao bounds (CRBs) for the estimates of these unknown parameters, and show that the bounds are functions of the PMU locations and of the contribution of each measurement variable in the combined output. We finally state the condition for finding the optimal PMU location that guarantees the tightest CRB, and, therefore, the most accurate swing model for the system.Index Terms-Cramer-Rao bound (CRB), parameter estimation, phasor measurment unit (PMU) allocation, pseudomeasurements, swing dynamics.

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“…For this reason researchers have proposed hybrid state estimation schemes [4], integrating both PMU and SCADA data. Some of these methods incorporate the PMU measurements into the iterative state estimation updates [5]- [7], while others use PMU data to refine the estimates obtained from SCADA data [8], [9]. The estimation procedure becomes, again, iterative and, therefore, a rapid convergence to an estimation error that is lower than what PMUs alone can provide, is crucial to render these hybrid systems useful.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Phasor Measurement Placement in Hybrid State Estimation Via Gauss–Newton

Scaglione

Chang

2014

IEEE Trans. Power Syst.

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In this paper, we study the placement of Phasor Measurement Units (PMU) for enhancing hybrid state estimation via the traditional Gauss-Newton method, which uses measurements from both PMU devices and Supervisory Control and Data Acquisition (SCADA) systems. To compare the impact of PMU placements, we introduce a useful metric which accounts for three important requirements in power system state estimation: convergence, observability and performance (COP). Our COP metric can be used to evaluate the estimation performance and numerical stability of the state estimator, which is later used to optimize the PMU locations. In particular, we cast the optimal placement problem in a unified formulation as a semidefinite program (SDP) with integer variables and constraints that guarantee observability in case of measurements loss. Last but not least, we propose a relaxation scheme of the original integer-constrained SDP with randomization techniques, which closely approximates the optimum deployment. Simulations of the IEEE-30 and 118 systems corroborate our analysis, showing that the proposed scheme improves the convergence of the state estimator, while maintaining optimal asymptotic performance.

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Recent experience with a hybrid SCADA/PMU on-line state estimator

Cited by 26 publications

References 3 publications

State estimation and bad data processing for systems including PMU and SCADA measurements

State estimation and bad data processing for systems including PMU and SCADA measurements

Optimal Measurement Allocation Algorithms for Parametric Model Identification of Power Systems

A Framework for Phasor Measurement Placement in Hybrid State Estimation Via Gauss–Newton

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