Statistical and Numerical Robust State Estimator for Heavily Loaded Power Systems

Zhao, Junbo; Mili, Lamine; Pires, Robson Celso

doi:10.1109/tpwrs.2018.2849325

Cited by 29 publications

(24 citation statements)

References 22 publications

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“…GN iteratively approximates the objective by linearizing (1) at the latest solution. This iterative linearization procedure, though computationally efficient if convergent, can be potentially divergent under heavy loading or bad data conditions; see e.g., [4], [19]. One approach to tackle the nonlinearity is to introduce the outer-product matrix V := vv H ∈ C N ×N , consisting of all quadratic terms involving v. This way, each measurement in (1) is linearly related to V, as given by…”

Section: Problem Formulationmentioning

confidence: 99%

“…In addition to convergence guarantees and speed, it is truly important to consider practical issues in SE, such as i) robustness to bad data (outliers), ii) incorporation of additional meter types, and iii) multi-area implementation. Traditionally, outliers arise in SE due to data contamination, meter failure and synchronization issues [3], [4], [20]. More recently, malicious cyber attacks [24] and topology errors [6] can also contribute to SE outliers.…”

Section: Practical Extensions For Gd-se Solversmentioning

confidence: 99%

“…There is also increasing trend to consider multi-area SE among different control centers [5], [12], [25]. All these practical concerns have been shown to potentially worsen the convergence issue of GN-SE method; see e.g., [4]- [6]. Due to page limit, we focus on discussing the first two extensions for GD-SE.…”

Section: Practical Extensions For Gd-se Solversmentioning

confidence: 99%

“…We first test all methods on the LS objective function (4). The measurements include all bus voltage magnitudes, and all active and reactive line flows at the 'from' end.…”

Section: ) Test Case 1 On Ls-se Objectivementioning

confidence: 99%

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Fast Nonconvex SDP Solvers for Large-Scale Power System State Estimation

Lan

Zhu

Guan

2020

IEEE Trans. Power Syst.

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Fast power system state estimation (SE) solution is of paramount importance for achieving real-time decision making in power grid operations. Semidefinite programming (SDP) reformulation has been shown effective to obtain the global optimum for the nonlinear SE problem, while suffering from high computational complexity. Thus, we leverage the recent advances in nonconvex SDP approach that allows for the simple first-order gradient-descent (GD) updates. Using the power system model, we can verify that the SE objective function enjoys nice properties (strongly convex, smoothness) which in turn guarantee a linear convergence rate of the proposed GD-based SE method. To further accelerate the convergence speed, we consider the accelerated gradient descent (AGD) extension, as well as their robust versions under outlier data and a hybrid GD-based SE approach with additional synchrophasor measurements. Numerical tests on the IEEE 118-bus, 300-bus and the synthetic ACTIVSg2000bus systems have demonstrated that FGD-SE and AGD-SE, can approach the near-optimal performance of the SDP-SE solution at significantly improved computational efficiency, especially so for AGD-SE.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Practical Extensions For Gd-se Solversmentioning

confidence: 99%

Section: Practical Extensions For Gd-se Solversmentioning

confidence: 99%

“…We first test all methods on the LS objective function (4). The measurements include all bus voltage magnitudes, and all active and reactive line flows at the 'from' end.…”

Section: ) Test Case 1 On Ls-se Objectivementioning

confidence: 99%

See 2 more Smart Citations

Fast Nonconvex SDP Solvers for Large-Scale Power System State Estimation

Lan

Zhu

Guan

2020

IEEE Trans. Power Syst.

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“…Commonly used PSSE criteria include the weighted leastsquares (WLS) and the least-absolute value (LAV) [9]. Other enhanced estimators for robustness consist of the Schweppe-Huber generalized M-estimator [10], [11], [12], as well as the least-median and the least-trimmed squares state estimators [10]. The WLS criterion would coincide with the maximum likelihood criterion when additive white Gaussian noise is assumed.…”

Section: Introductionmentioning

confidence: 99%

Robust and Scalable Power System State Estimation via Composite Optimization

Wang

Giannakis

Chen

2019

IEEE Trans. Smart Grid

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In today's cyber-enabled smart grids, high penetration of uncertain renewables, purposeful manipulation of meter readings, and the need for wide-area situational awareness, call for fast, accurate, and robust power system state estimation. The least-absolute-value (LAV) estimator is known for its robustness relative to the weighted least-squares (WLS) one. However, due to nonconvexity and nonsmoothness, existing LAV solvers based on linear programming are typically slow, hence inadequate for real-time system monitoring. This paper develops two novel algorithms for efficient LAV estimation, which draw from recent advances in composite optimization. The first is a deterministic linear proximal scheme that handles a sequence of (5 ∼ 10 in general) convex quadratic problems, each efficiently solvable either via off-the-shelf toolboxes or through the alternating direction method of multipliers. Leveraging the sparse connectivity inherent to power networks, the second scheme is stochastic, and updates only a few entries of the complex voltage state vector per iteration. In particular, when voltage magnitude and (re)active power flow measurements are used only, this number reduces to one or two, regardless of the number of buses in the network. This computational complexity evidently scales well to large-size power systems. Furthermore, by carefully minibatching the voltage and power flow measurements, accelerated implementation of the stochastic iterations becomes possible. The developed algorithms are numerically evaluated using a variety of benchmark power networks. Simulated tests corroborate that improved robustness can be attained at comparable or markedly reduced computation times for medium-or large-size networks relative to existing alternatives.

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