A robust state estimator for medium voltage distribution networks

Wu, Jianzhong; He, Yan; Jenkins, Nick

doi:10.1109/tpwrs.2012.2215927

Cited by 140 publications

(108 citation statements)

References 15 publications

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“…Another load estimator based on a ML technique is proposed in [96], in which the load model developed works in a closed loop and has the capability of training itself as new measurement data comes in and thus, enhances the performance of DSSE. Closed loop models are developed for the load forecast in [97,98].…”

Section: Intelligent Load Forecast Techniques For 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: Intelligent Load Forecast Techniques For 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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“…Different methods, such as weighted least square (WLS) [34], Bayesian network [35], graph theory [36], and machine learning [37], are proposed for state estimation. (3) Volt/VAR management: voltage and reactive power management is essential for utilities to minimize power losses while maintaining an acceptable voltage profile along the distribution feeder under various loading conditions [38,39].…”

Section: Smart Metering Technologymentioning

confidence: 99%

Smart Distribution Systems

Jiang

Liu

2016

Energies

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Abstract:The increasing importance of system reliability and resilience is changing the way distribution systems are planned and operated. To achieve a distribution system self-healing against power outages, emerging technologies and devices, such as remote-controlled switches (RCSs) and smart meters, are being deployed. The higher level of automation is transforming traditional distribution systems into the smart distribution systems (SDSs) of the future. The availability of data and remote control capability in SDSs provides distribution operators with an opportunity to optimize system operation and control. In this paper, the development of SDSs and resulting benefits of enhanced system capabilities are discussed. A comprehensive survey is conducted on the state-of-the-art applications of RCSs and smart meters in SDSs. Specifically, a new method, called Temporal Causal Diagram (TCD), is used to incorporate outage notifications from smart meters for enhanced outage management. To fully utilize the fast operation of RCSs, the spanning tree search algorithm is used to develop service restoration strategies. Optimal placement of RCSs and the resulting enhancement of system reliability are discussed. Distribution system resilience with respect to extreme events is presented. Test cases are used to demonstrate the benefit of SDSs. Active management of distributed generators (DGs) is introduced. Future research in a smart distribution environment is proposed.

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“…The pseudo measurements are typically obtained from historical data or it can be obtained from low Accepted Paper Accepted Paper voltage smart meters [10]. The virtual measurements are perfect high quality zero injections obtained from nodes without loads and generators.…”

Section: General Aspects Of Distribution Systemsmentioning

confidence: 99%

Assessing the statistical consistency of the AMB State Estimator in distribution systems

Almeida

Schincariol

Ochoa

2015

2015 IEEE PES Innovative Smart Grid Technologies Latin America (ISGT LATAM)

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-In this paper the statistical consistency of the singlephase Admittance Matrix-Based State Estimator (AMB SE) originally proposed for distribution systems is for the first time assessed and compared to that of the widely used Traditional SE (TSE). The AMB SE is an interesting alternative for distribution systems given the use of a constant Gain matrix, thus simplifying its implementation and significantly reducing the computational time. However, it is crucial to statistically assess its consistency so as to ensure estimated values and variances (outputs) are in agreement with the specified measurements and variances (inputs). This is carried out using a Monte Carlo analysis and a generic 16-bus UK distribution system. Despite the conversion from actual to equivalent measurements required by the AMB SE, as well as the approximations regarding the variances of the equivalent measurements, the results demonstrate that the single-phase AMB SE is as consistent and precise as the TSE.

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A robust state estimator for medium voltage distribution networks

Cited by 140 publications

References 15 publications

Distribution system state estimation-A step towards smart grid

Distribution system state estimation-A step towards smart grid

Smart Distribution Systems

Assessing the statistical consistency of the AMB State Estimator in distribution systems

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