Distribution Grid State Estimation from Compressed Measurements

Alam, Samiul; Natarajan, Balasubramaniam; Pahwa, Anil

doi:10.1109/tsg.2013.2296534

Cited by 102 publications

(57 citation statements)

References 37 publications

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“…In [117], measurement data from smart meters is used for estimating various network variables such as voltages and line flows etc. Another method that uses compressed smart meter data and data from DERs, is proposed in [118].…”

Section: Inclusion Of Smart Meter Measurement Data In Dssementioning

confidence: 99%

Distribution system state estimation-A step towards smart grid

Ahmad

Rasool

Özsoy

et al. 2018

Renewable and Sustainable Energy Reviews

143

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Section: Inclusion Of Smart Meter Measurement Data In Dssementioning

confidence: 99%

Distribution system state estimation-A step towards smart grid

Ahmad

Rasool

Özsoy

et al. 2018

Renewable and Sustainable Energy Reviews

143

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“…This data can be used to better understand and model the behaviors of distribution network loads, allowing to improve load estimation techniques, and ultimately, DSSE accuracy [4]. Studies have been made into the incorporation of smart meter data in DSSE to estimate flows, voltages, and losses in low voltage distribution networks [9][10][11][12].…”

Section: B Use Of Measurementsmentioning

confidence: 99%

“…Various techniques have been investigated for DSSE in literature, such as Weighted Least Squares (WLS) based SE [1], fuzzy SE [5] and branch-based SE [6], and so on. Besides, various types of data in distribution networks including Advanced Metering Infrastructure data have been extensively explored for the purpose of DSSE [4,[7][8][9][10][11][12][13].…”

mentioning

confidence: 99%

Pathway to cost-efficient state estimation of future distribution networks

Liao

Milanović

2016

2016 IEEE Power and Energy Society General Meeting (PESGM)

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Abstract-This paper provides a detailed overview of the techniques and applications related to distribution system state estimation (DSSE), together with the classification of various types of state estimation. The paper also provides the state-of-art techniques applied in DSSE including forecasted-aided state estimation, close-loop DSSE methods, the application of computation intelligence in DSSE and the use of smart meters and phasor measurement units in load estimation. As more and more active elements and functionalities will be integrated in future distribution network, e.g. demand-side management, the increased penetration of decentralized generation and dynamically controlled storage/devices, DSSE will be one of the critical functionalities for secure operation of future distribution networks. As a pathway to cost-efficient DSSE for future distribution networks, this paper addresses the characteristics of future distribution grids that will affect DSSE, and also discusses the techniques and data that can be used to better understand/model the network behaviors and load profiles in order to ultimately improve the accuracy of DSSE.

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“…These measurements can be efficiently aggregated using the compressed sensing approach, which exploits the correlated characteristics of renewable energy sources and loads, geographically distributed over the distribution network [27]. Once transmitted to the fusion center, the compressive measurements can be directly used for static state estimation [28]. This approach not only reduces the communication overhead but also allows efficient storage of the raw measurements obtained in real-time from an ever expanding network of power distribution.…”

Section: Holonic Framework For State Estimationmentioning

confidence: 99%

“…The average home-level estimation error for voltage magnitudes are calculated in terms of mean absolute error (MAE) The average error in voltage angle estimation is represented using mean integrated absolute error (MIAE) [28] …”

Section: A Case Study Of 560 Node Distribution Networkmentioning

confidence: 99%

Goal-Based Holonic Multiagent System for Operation of Power Distribution Systems

Pahwa

DeLoach

Natarajan

et al. 2015

IEEE Trans. Smart Grid

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Large-scale integration of rooftop solar power generation is transforming traditionally passive power distribution systems into active ones. High penetration of such devices creates new dynamics for which the current power distribution systems are inadequate. The changing paradigm of power distribution system requires it to be operated as cyber-physical system. A goal-based holonic multiagent system (HMAS) is presented in this paper to achieve this objective. This paper provides details on design of the HMAS for operation of power distribution systems. Various operating modes and associated goals are discussed. Finally, the role of HMAS is demonstrated for two applications in distribution systems. The first one is associated with control of reactive power at solar photovoltaic installations at individual homes for optimal operation of the system. The second deals with the state estimation of the system leveraging different measurements available from smart meters at homes.Index Terms-Cyber-physical system (CPS), multiagent system (MAS), optimization, power distribution system, smart grid, solar power, state estimation.

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Distribution Grid State Estimation from Compressed Measurements

Cited by 102 publications

References 37 publications

Distribution system state estimation-A step towards smart grid

Distribution system state estimation-A step towards smart grid

Pathway to cost-efficient state estimation of future distribution networks

Goal-Based Holonic Multiagent System for Operation of Power Distribution Systems

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