Bayesian Approach for Distribution System State Estimation With Non-Gaussian Uncertainty Models

Pegoraro, Paolo Attilio; Angioni, Andrea; Pau, Massimiliano; Monti, Antonello; Muscas, Carlo; Ponci, Ferdinanda; Sulis, Sara

doi:10.1109/tim.2017.2728398

Cited by 67 publications

(51 citation statements)

References 31 publications

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“…SM measurements can be helpful also to build pseudo-measurements; this is not a simple task because it involves load and generation forecasting (as in [75], see the discussion in the next section), data modeling and, even though it is often neglected, uncertainty evaluation. Pseudo-measurements are built by means of statistical analysis, AI, and cluster analysis methods [71,76,77], while ad-hoc techniques must be designed to include complex non-Gaussian models in DSSE algorithms [78].…”

Section: Distribution System State Estimationmentioning

confidence: 99%

A Multidisciplinary Approach for the Development of Smart Distribution Networks

et al. 2018

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Electric power systems are experiencing relevant changes involving the growing penetration of distributed generation and energy storage systems, the introduction of electric vehicles, the management of responsive loads, the proposals for new energy markets and so on. Such an evolution is pushing a paradigm shift that is one of the most important challenges in power network design: the management must move from traditional planning and manual intervention to full “smartization” of medium and low voltage networks. Peculiarities and criticalities of future power distribution networks originate from the complexity of the system which includes both the physical aspects of electric networks and the cyber aspects, like data elaboration, feature extraction, communication, supervision and control; only fully integrated advanced monitoring systems can foster this transition towards network automation. The design and development of such future networks require distinct kinds of expertise in the industrial and information engineering fields. In this context, this paper provides a comprehensive review of current challenges and multidisciplinary interactions in the development of smart distribution networks. The aim of this paper is to discuss, in an integrated and organized manner, the state of the art while focusing on the need for interaction between different disciplines and highlighting how innovative and future-proof outcomes of both research and practice can only emerge from a coordinated design of all the layers in the smart distribution network architecture.

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Section: Distribution System State Estimationmentioning

confidence: 99%

A Multidisciplinary Approach for the Development of Smart Distribution Networks

et al. 2018

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“…The establishment and analysis of the whole model are based on the Bayesian probability theorem [13] as shown in Equation (1):Pfalse(A|Bfalse)=Pfalse(B|Afalse)Pfalse(Afalse)/Pfalse(Bfalse),Pfalse(A|Bfalse)∝Pfalse(B|Afalse)Pfalse(Afalse) where A represents the normal posture of the tester, and B represents the triangle area composed of three barycenters.…”

Section: Methodsmentioning

confidence: 99%

Classification for Human Balance Capacity Based on Visual Stimulation under a Virtual Reality Environment

Jin

Xie

Xiao

et al. 2019

Sensors

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The normal and disordered people balance ability classification is a key premise for rehabilitation training. This paper proposes a multi-barycentric area model (MBAM), which can be applied for accurate video analysis based classification. First, we have invited fifty-three subjects to wear an HTC (High Tech Computer Corporation) VIVE (Very Immersive Virtual Experience) helmet and to walk ten meters while seeing a virtual environment. The subjects’ motion behaviors are collected as our balance ability classification dataset. Secondly, we use background differential algorithm and bilateral filtering as the preprocessing to alleviate the video noise and motion blur. Inspired by the balance principle of a tumbler, we introduce a MBAM model to describe the body balancing condition by computing the gravity center of a triangle area, which is surrounded by the upper, middle and lower parts of the human body. Finally, we can obtain the projection coordinates according to the center of gravity of the triangle, and get the roadmap of the subjects by connecting those projection coordinates. In the experiments, we adopt four kinds of metrics (the MBAM, the area variance, the roadmap and the walking speed) innumerical analysis to verify the effect of the proposed method. Experimental results show that the proposed method can obtain a more accurate classification for human balance ability. The proposed research may provide potential theoretical support for the clinical diagnosis and treatment for balance dysfunction patients.

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“…Using Mean Squared Estimator (MSE) an analytic SE formulation is obtained in [29] which does not depend on Gaussian uncertainty assumptions and is capable of bad data measurement detection. A similar estimator is used in [30], where a Bayesian alternative to WLS is proposed. It is shown that the Bayesian approach has specifically better performance in presence of non-Gaussian uncertainty.…”

Section: Fundamentals Of Sementioning

confidence: 99%

A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems

Dehghanpour

Wang

et al. 2019

IEEE Trans. Smart Grid

357

173

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This paper presents a review of the literature on State Estimation (SE) in power systems. While covering some works related to SE in transmission systems, the main focus of this paper is Distribution System State Estimation (DSSE). The paper discusses a few critical topics of DSSE, including mathematical problem formulation, application of pseudomeasurements, metering instrument placement, network topology issues, impacts of renewable penetration, and cyber-security. Both conventional and modern data-driven and probabilistic techniques have been reviewed. This paper can provide researchers and utility engineers with insights into the technical achievements, barriers, and future research directions of DSSE.

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Bayesian Approach for Distribution System State Estimation With Non-Gaussian Uncertainty Models

Cited by 67 publications

References 31 publications

A Multidisciplinary Approach for the Development of Smart Distribution Networks

A Multidisciplinary Approach for the Development of Smart Distribution Networks

Classification for Human Balance Capacity Based on Visual Stimulation under a Virtual Reality Environment

A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems

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