Power System State Estimation: Detection of Topological Errors

There is an increasing need for monitoring and controlling uncertainties brought by distributed energy resources in distribution grids. For such goal, accurate multi-phase topology is the basis for correlating measurements in unbalanced distribution networks. Unfortunately, such topology knowledge is often unavailable due to limited investment, especially for low-voltage distribution grids. Also, the bus phase labeling information is inaccurate due to human errors or outdated records. For this challenge, this paper utilizes smart meter data for an informationtheoretic approach to learn the topology of distribution grids. Specifically, multi-phase unbalanced systems are converted into symmetrical components, namely positive, negative, and zero sequences. Then, this paper proves that the Chow-Liu algorithm finds the topology by utilizing power flow equations and the conditional independence relationships implied by the radial multiphase structure of distribution grids with the presence of incorrect bus phase labels. At last, by utilizing Carson's equation, this paper proves that the bus phase connection can be correctly identified using voltage measurements. For validation, IEEE systems are simulated using three real data sets. The simulation results demonstrate that the algorithm is highly accurate for finding multi-phase topology even with strong load unbalancing condition and DERs. This ensures close monitoring and controlling DERs in distribution grids.

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“…Given ΔI 1 ⊥ΔI 4 , according to (5) and 6, ΔV 3 and ΔV 4 are conditionally independent given ΔV 1 , ΔV 2 , and ΔV 5 . In 5…”

Section: P(δvmentioning

confidence: 99%

Unbalanced multi‐phase distribution grid topology estimation and bus phase identification

et al. 2019

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“…The sparsity structure of J inherently contains the most up-to-date network topology and corresponding parameters. Topology errors have long been cited as a cause of inaccurate state estimation results [2,3]. Moreover, the Jacobian matrix, in practice, may be out-ofdate due to erroneous records, faulty telemetry from remotely monitored circuit breakers, or unexpected operating conditions resulting from unforeseen equipment failure.…”

Section: Introductionmentioning

confidence: 99%

Data-driven Estimation of the Power Flow Jacobian Matrix in High Dimensional Space

He¹,

Chu²,

Qiu³

et al. 2019

Preprint

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The Jacobian matrix is the core part of power flow analysis, which is the basis for power system planning and operations. This paper estimates the Jacobian matrix in high dimensional space. Firstly, theoretical analysis and model-based calculation of the Jacobian matrix are introduced to obtain the benchmark value. Then, the estimation algorithms based on leastsquared errors and the deviation estimation based on the neural network are studied in detail, including the theories, equations, derivations, codes, advantages and disadvantages, and application scenes. The proposed algorithms are data-driven and sensitive to up-to-date topology parameters and state variables. The efforts are validate by comparing the results to benchmark values.

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“…Thus, it is impossible to associate statistically values to those switching components. This is the reason why most of the topology error identification methods attempt to indirectly infer the presence George N. Korres of topology errors from their impact on analog measurements [1]− [11].…”

Section: Introductionmentioning

confidence: 99%

A state estimation algorithm for monitoring topology changes in distribution systems

Korres

Manousakis

2012

2012 IEEE Power and Energy Society General Meeting

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This paper proposes a state estimation algorithm for the monitoring of topological changes in distribution networks, using real measurements at substations, pseudo measurements at load and distributed generation buses, and zero injections at transition buses. This is accomplished by a generalized state estimation formulation, based on a weighted least squares approach with equality constraints, which involves both real and pseudo analog measurements and operational constraints for the switching devices. Estimates of state variables and switching device statuses are simultaneously provided. The normalized residual test is used to effectively identify gross errors in analog and topological data. The network splitting problem, caused by switching operations, is also investigated by introducing appropriate pseudo measurements. An advantage over previous methods is that the proposed approach eliminates the need of repeated state estimation runs for alternative hypothesis evaluation. A 17-bus distribution network is employed to demonstrate its effectiveness.Index Terms--Distribution system, network topology, network split, operational constraint, state estimation, switching device. Nikolaos M. Manousakis received the B.S. and the Dipl. Eng. degrees from Technological Educational

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Power System State Estimation: Detection of Topological Errors

Cited by 87 publications

References 6 publications

Unbalanced multi‐phase distribution grid topology estimation and bus phase identification

Unbalanced multi‐phase distribution grid topology estimation and bus phase identification

Data-driven Estimation of the Power Flow Jacobian Matrix in High Dimensional Space

A state estimation algorithm for monitoring topology changes in distribution systems

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