Real time recursive parameter estimation in energy management systems

Slutsker, I.W.; Mokhtari, Soroush; Clements, K.A.

doi:10.1109/59.535680

Cited by 115 publications

(51 citation statements)

References 5 publications

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“…With the help of GPS timing devices, the existing AMIs could be easily upgraded to have phasor measurement capability in the future. For example, [7]- [12] use such a data-driven idea. But, they assume that line measurements are always available, which is usually not widely applicable in distribution grids.…”

Section: Introductionmentioning

confidence: 99%

PaToPa: A Data-Driven Parameter and Topology Joint Estimation Framework in Distribution Grids

Yu¹,

Weng

Rajagopal³

2018

IEEE Trans. Power Syst.

165

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Abstract-The increasing integration of distributed energy resources (DERs) calls for new planning and operational tools. However, such tools depend on system topology and line parameters, which may be missing or inaccurate in distribution grids. With abundant data, one idea is to use linear regression to find line parameters, based on which topology can be identified. Unfortunately, the linear regression method is accurate only if there is no noise in both the input measurements (e.g., voltage magnitude and phase angle) and output measurements (e.g., active and reactive power). For topology estimation, even with a small error in measurements, the regression-based method is incapable of finding the topology using non-zero line parameters with a proper metric. To model input and output measurement errors simultaneously, we propose the error-in-variables (EIV) model in a maximum likelihood estimation (MLE) framework for joint line parameter and topology estimation. While directly solving the problem is NP-hard, we successfully adapt the problem into a generalized low-rank approximation problem via variable transformation and noise decorrelation. For accurate topology estimation, we let it interact with parameter estimation in a fashion that is similar to expectation-maximization fashion in machine learning. The proposed PaToPa approach does not require a radial network setting and works for mesh networks. We demonstrate the superior performance in accuracy for our method on IEEE test cases with actual feeder data from South California Edison.

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Section: Introductionmentioning

confidence: 99%

PaToPa: A Data-Driven Parameter and Topology Joint Estimation Framework in Distribution Grids

Yu¹,

Weng

Rajagopal³

2018

IEEE Trans. Power Syst.

165

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“…An offline application has been recently reported in [9]. The second type of PEI methods is based on augmented state estimation (ASE) [10][11][12][13]. ASE methods augment the parameter errors to the state variables and obtain their estimated values through SE calculation.…”

Section: Introductionmentioning

confidence: 99%

A Fast Solution for the Lagrange Multiplier-Based Electric Power Network Parameter Error Identification Model

Guo

Zhang

et al. 2014

Energies

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Abstract:The Lagrange multiplier-based method is an effective network parameter error identification method. However, two full matrices with high-dimensions are involved in the calculation procedure; these create huge computational burdens for large-scale power systems. To solve this problem, a fast solution is proposed in this paper, where special treatment techniques for full matrices are used to dramatically improve the calculation efficiency. A practical parameter error identification program has been developed and used in many electric power control centers. In this paper, the results for test systems and on-site applications are given, which show that the proposed approach is very efficient.

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“…In a different approach, the Extended Kalman Filtering (EKF) theory has been applied for network parameter estimation [9], static state estimation [10], and dynamic state estimation [11]. However, EKF needs to collect recursively [12] by Nordman and Lehtonen.…”

Section: Introductionmentioning

confidence: 99%