State estimation of Active Distribution Networks: Comparison between WLS and iterated kalman-filter algorithm integrating PMUs

Sarri, Styliani; Paolone, Mario; Cherkaoui, Rachid; Borghetti, Alberto; Napolitano, Fabio; Nucci, C.A.

doi:10.1109/isgteurope.2012.6465871

Cited by 75 publications

(58 citation statements)

References 29 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

141

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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

141

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“…At the current stage, we assume that the location of the ST with storage is given. Voltage control requires the knowledge of the voltage levels of both the MV and LV buses, which are assumed known from, for example, PMU-based and smart meters-based monitoring infrastructure or integrated from state estimation algorithms in case of non complete observability, see e.g., [17], [18].…”

Section: A Problem Statementmentioning

confidence: 99%

“…As these functionalities are deployed in time horizons that vary between few hundreds of ms (fault management) to few tens of seconds (voltage control and line congestions), they might require the knowledge of the network state with relatively high refresh rates. In this respect, the massive adoption in ADNs of end-user smart metering and/or dedicated remote terminal units (such as Phasor Measurement Units) enables EMS to have access to the grid state with high time resolution, accuracy and low latency [3]. As a consequence, these monitoring technologies enables the definition of optimal control strategies that also take advantage of new flexible elements composed by grid-connected battery energy storage systems (BESS).…”

Section: Introductionmentioning

confidence: 99%

Enhancing the provision of ancillary services from storage systems using smart transformer and smart meters

Sossan

Christakou

Paolone

et al. 2017

2017 IEEE 26th International Symposium on Industrial Electronics (ISIE)

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Abstract-The Smart Transformer, a solid-state transformer with control and communication functionalities, can be the ideal solution for integrating storage into the grid. By leveraging the knowledge of the grid state of distribution grids thanks to smart meters and/or dedicated remote terminal units, in the paper, it is proposed a control strategy for a MV/LV smart transformer (ST) with integrated storage to achieve: i) dispatched-by-design operation of the LV network by controlling the ST active power set-point on the MV power converter, and ii) voltage regulation of both MV and LV networks by controlling the reactive power injections of both LV and MV converter. The former is achieved by dispatching the active power flow of the LV network according to a profile established the day before the operation, called dispatch plan, with the objective of reducing the amount of regulating power required to operate the grid. It is based on the use of forecast to compute a dispatch plan, and a tracking problem to compensate in real-time the mismatch between realization and dispatch plan by taking advantage of the storage capacity. The latter is achieved by using sensitivity coefficients, which are calculated from the state of the grid and integrating the information on the network topology. The problem formulation is given in the paper, and the proof-ofconcept is shown by simulation using the IEEE 34 nodes test feeder and the CIGRE Low Voltage reference network.

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“…The measurement noise covariance matrix R and the process noise covariance matrix Q can significantly affect the RTSE accuracy as discussed in [8]. R represents the accuracies of the measurement devices, whereas Q is related to uncertainty introduced by the process model to predict the system state.…”

Section: E Discrete Kalman Filter State Estimatormentioning

confidence: 99%

“…Within this context, the development of high-performance RTSEs is facilitated by the use of Phasor Measurement Units (PMUs) (e.g., [7], [8]) that, nowadays, are able to accurately estimate synchrophasors, stream them at 50 or 60 frames-persecond (fps) [9] and be resilient against fast power systems transients and presence of highly distorted waveforms (e.g., [10]). …”

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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State estimation of Active Distribution Networks: Comparison between WLS and iterated kalman-filter algorithm integrating PMUs

Cited by 75 publications

References 29 publications

Distribution system state estimation-A step towards smart grid

Distribution system state estimation-A step towards smart grid

Enhancing the provision of ancillary services from storage systems using smart transformer and smart meters

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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