R
Ravindra Singh
Researcher at Argonne National Laboratory
Publications - 33
Citations - 2492
Ravindra Singh is an academic researcher from Argonne National Laboratory. The author has contributed to research in topics: Mixture model & Gaussian process. The author has an hindex of 16, co-authored 31 publications receiving 2110 citations. Previous affiliations of Ravindra Singh include Indian Institute of Science & Imperial College London.
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Minimum Loss Network Reconfiguration Using Mixed-Integer Convex Programming
TL;DR: In this paper, a mixed-integer conic programming formulation for the minimum loss distribution network reconfiguration problem is proposed, which employs a convex representation of the network model which is based on the conic quadratic format of the power flow equations.
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Distribution System State Estimation Using an Artificial Neural Network Approach for Pseudo Measurement Modeling
TL;DR: This paper presents an alternative approach to pseudo measurement modeling in the context of distribution system state estimation (DSSE), where pseudo measurements are generated from a few real measurements using artificial neural networks in conjunction with typical load profiles.
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Statistical Representation of Distribution System Loads Using Gaussian Mixture Model
TL;DR: In this article, a probabilistic approach for statistical modeling of the loads in distribution networks is presented, where the probability density functions (pdfs) of loads at different buses show a number of variations and cannot be represented by any specific distribution.
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Choice of estimator for distribution system state estimation
TL;DR: A statistical framework is introduced to assess the suitability of various state estimation methodologies for the purpose of distribution system state estimation (DSSE) and the existing algorithms adopted in the transmission system SE are reconfigured for the distribution system.
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Measurement Placement in Distribution System State Estimation
TL;DR: In this paper, a technique for meter placement for the purpose of improving the quality of voltage and angle estimates across a network is introduced, which is based on the sequential improvement of a bivariate probability index governing relative errors in voltage and angles at each bus.