A Linear State Estimation Formulation for Smart Distribution Systems

Haughton, D.; Heydt, G.T.

doi:10.1109/tpwrs.2012.2212921

Cited by 212 publications

(111 citation statements)

References 18 publications

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“…They could prove to be more useful in DSs, where DSSE would likely face more challenges like computational complexity and estimation accuracy. In [108,109] and [110,111], the authors have worked on the incorporation of PMU in the DSSE algorithm. Beside all these studies, the deployment of PMUs in DNs is not economical.…”

Section: Incorporation Of Pmu Measurements In Dssementioning

confidence: 99%

Distribution system state estimation-A step towards smart grid

Ahmad

Rasool

Özsoy

et al. 2018

Renewable and Sustainable Energy Reviews

149

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Section: Incorporation Of Pmu Measurements In Dssementioning

confidence: 99%

Distribution system state estimation-A step towards smart grid

Ahmad

Rasool

Özsoy

et al. 2018

Renewable and Sustainable Energy Reviews

149

View full text Add to dashboard Cite

“…In reality, the system states are not always available due probably to physical constraints, technological restrictions or expensive cost for measuring [5,7,12]. Therefore, the state estimation problem has recently received tremendous research interest [2,3,9]. To better understand the complex networks and achieve certain engineering requirements, it is also of great significance to estimate the states of the network nodes by using the available measurements.…”

Section: Introductionmentioning

confidence: 99%

A variance-constrained approach to recursive state estimation for time-varying complex networks with missing measurements

et al. 2016

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In this paper, the recursive state estimation problem is investigated for an array of discrete time-varying coupled stochastic complex networks with missing measurements. A set of random variables satisfying certain probabilistic distributions is introduced to characterize the phenomenon of the missing measurements, where each sensor can have individual missing probability. The Taylor series expansion is employed to deal with the nonlinearities and the high-order terms of the linearization errors are estimated. The purpose of the addressed state estimation problem is to design a time-varying state estimator such that, in the presence of the missing measurements and the random disturbances, an upper bound of the estimation error covariance can be guaranteed and the explicit expression of the estimator parameters is given. By using the Riccatilike difference equations approach, the estimator parameter is characterized by the solutions to two Riccati-like difference equations. It is shown that the obtained upper bound is minimized by the designed estimator parameters and the proposed state estimation algorithm is of a recursive form suitable for online computation. Finally, an illustrative example is provided to demonstrate the feasibility and effectiveness of the developed state estimation scheme.

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“…As known, the use of PMU-only measurements enables the formulation of the SE problem in a linear way (e.g., [11]- [13]). The data measured by the PMUs can be acquired and stored in a Real-Time (RT) database, provided by Phasor Data Concentrators (PDCs) suitably coupled with the RTSE.…”

Section: Introductionmentioning

confidence: 99%

A Hardware-in-the-Loop test platform for the performance assessment of a PMU-based Real-Time State Estimator for Active Distribution Networks

Sarri

Pignati

Romano

et al. 2015

2015 IEEE Eindhoven PowerTech

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Abstract-The paper describes the development of a Hardwarein-the-Loop (HIL) test platform for the performance assessment of a PMU-based sub-second linear Real-Time State Estimator (RTSE) for Active Distribution Networks (ADNs). The estimator relies on the availability of data coming from Phasor Measurement Units (PMUs) and can be applied to both balanced and unbalanced ADNs. The paper first illustrates the architecture of the experimental HIL setup that has been fully designed by the Authors. It consists of a Real-Time Simulator (RTS) that models the electrical network model as well as the measurement infrastructure composed by virtual PMUs. These virtual devices stream their data to a real Phasor Data Concentrator (PDC) suitably coupled with a Discrete Kalman Filter State Estimator (DKF-SE). By using this experimental setup, the paper discusses the performance assessment of the whole process in terms of estimation accuracy and time latencies. In the RTS, a real ADN located in the Netherlands has been modeled together with the associated PMUs.

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A Linear State Estimation Formulation for Smart Distribution Systems

Cited by 212 publications

References 18 publications

Distribution system state estimation-A step towards smart grid

Distribution system state estimation-A step towards smart grid

A variance-constrained approach to recursive state estimation for time-varying complex networks with missing measurements

A Hardware-in-the-Loop test platform for the performance assessment of a PMU-based Real-Time State Estimator for Active Distribution Networks

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