Distribution circuit state estimation using a probabilistic approach

Ghosh, Ashish; Lubkeman, David; Downey, Matthew; Jones, Richard H.

doi:10.1109/59.574922

Cited by 176 publications

(94 citation statements)

References 10 publications

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“…As a result, they proposed the beta distribution function. A similar research effort was reported by Ghosh et al [6] in distribution system state estimation problem. They validated various models such as normal, log-normal, and beta distribution through chi-square goodness of fit test.…”

supporting

confidence: 63%

Statistical representation of distribution system loads using Gaussian Mixture Model

Singh

Pal

Jabr

2010

IEEE PES General Meeting

103

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Abstract-This paper presents a probabilistic approach for statistical modeling of the loads in distribution networks. In a distribution network, the probability density functions (pdfs) of loads at different buses show a number of variations and cannot be represented by any specific distribution. The approach presented in this paper represents all the load pdfs through Gaussian mixture model (GMM). The expectation maximization (EM) algorithm is used to obtain the parameters of the mixture components. The performance of the method is demonstrated on a 95-bus generic distribution network model. Index Terms-Expectation maximization (EM) algorithm,Gaussian mixture model, load profile.

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supporting

confidence: 63%

Statistical representation of distribution system loads using Gaussian Mixture Model

Singh

Pal

Jabr

2010

IEEE PES General Meeting

103

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“…This can be done by a variety of methods that have been proposed in literature [16][17][18][19]. Which method is most suitable for a particular network, has to be decided on a case by case basis.…”

Section: B Robust State Estimationmentioning

confidence: 99%

Robust state estimation in distribution networks

Brinkmann

Negnevisky

2016

2016 Australasian Universities Power Engineering Conference (AUPEC)

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Abstract-In this paper a new approach to state estimation in distribution networks is proposed. This approach is more robust against large uncertainties of the state estimation inputs than the conventional method. Traditionally, the goal of state estimation was to estimate the exact value of network parameters, such as voltages and currents. This works well in transmission networks where many real time measurements are available. In distribution networks, however, only few real-time measurements are available. This means that the estimated state can be significantly different from the actual network state. Therefore, the focus of the proposed robust state estimation is shifted from estimating the exact values of the network parameters to the confidence that these parameters are within their respective constraints. This approach is able to provide useful results for distribution network operation, even if large uncertainties are present in the estimated network state.

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“…For this purpose a number of methods are available in literature [9][10][11][12]. Since the PDF is a continuous function which ranges from plus infinity to negative infinity, a confidence level (CL) has to be applied.…”

Section: B Individually Determined Accuracy Recuirementmentioning

confidence: 99%

A practical approach to meter placement in distribution networks

Brinkmann

Scott

Bicevskis

et al. 2017

2017 IEEE Manchester PowerTech

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Abstract-This paper presents a novel approach to the optimal meter placement problem in distribution networks. Conventional methods place meters in order to achieve a predefined level of accuracy for the estimated parameters such as voltages and currents. However, there is no generic method to determine this desired accuracy value for the individual parameters. Hence, a uniform accuracy value is generally chosen. Considering that parameters which are not in proximity to their constraint do not need to be monitored with a high level of accuracy, using a uniform accuracy for every estimated parameter may not be optimal. The method proposed in this paper takes a new approach which accounts for the proximity of the estimated parameters to their constraints. This approach can potentially reduce the number of required metering devices compared to conventional meter placement methods, while providing a practical level of state estimation accuracy.Index Terms-Optimal meter placement, uncertainty, power system measurements, distribution network state estimation

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Distribution circuit state estimation using a probabilistic approach

Cited by 176 publications

References 10 publications

Statistical representation of distribution system loads using Gaussian Mixture Model

Statistical representation of distribution system loads using Gaussian Mixture Model

Robust state estimation in distribution networks

A practical approach to meter placement in distribution networks

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