Measurement Placement in Distribution System State Estimation

By Ravindra Singh, Bikash Chandra Pal, Richard B. Vinter. IEEE Transactions on Power Systems, 2009

259 citations recorded by the source corpus.

This paper introduces a technique for meter placement for the purpose of improving the quality of voltage and angle estimates across a network. The proposed technique is based on the sequential improvement of a bivariate probability index governing relative errors in voltage and angle at each bus. The meter placement problem is simplified by transforming it into a probability bound reduction problem, with the help of the two sided-Chebyshev inequality. A straightforward solution technique is proposed for the latter problem, based on the consideration of 2-sigma error ellipses. The benefits of the proposed technique are revealed by Monte Carlo simulations on a 95-bus UKGDS network model.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/04137aed12d8d7c5ec5d25eb369f6cfe80bf5f49

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In Electrical and Electronic Engineering

Citations in the corpus

A Mixed Integer Quadratic Programming Model for Topology Identification in Distribution Network (2016, methodology): “For a given maximum measurement error (denoted as ) in percentage about the mean , the standard deviation of the error can be computed as follows [6]:”
A Three-Phase Optimal Power-Flow Algorithm to Mitigate Voltage Unbalance (2013, background): “, [2]–[6], reconfiguration algorithms [7], [8], compensation devices to attain loss minimization [9], distributed generation impacts [10], [11], state estimation [12] and voltage unbalances [13]–[18], among others.”
Distribution System State Estimation Using an Artificial Neural Network Approach for Pseudo Measurement Modeling (2012, background): “), in accordance with the optimal measurement placement algorithm presented in [24].”
Trade-Offs in PMU Deployment for State Estimation in Active Distribution Grids (2012, background): “In the literature, two different approaches are followed for meter placement in distribution grids: determining theminimum number of measurement points that ensures compliance with fixed accuracy limits ([3], [12]) or performing a space exploration, with a given number ofmeasurements, to reach a pse”
A Probabilistic Approach to Observability of Distribution Networks (2017, background): “The standard deviation of a measurement is calculated as the product of one-third of the measurement accuracy and the expected value [33].”
Meter Placement for Distribution System State Estimation: An Ordinal Optimization Approach (2011, methodology): “The reasons are, first, that the algorithm is based on exact calculations of the relative error violation probabilities, rather than Chebychev bound approximations of [6].”
A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems (2018, unknown): “dress this problem, including improving system observability, minimizing installation/maintenance costs, bad data detection capability, and improving the DSSE accuracy [70] [41] [71] [72] [73] [74] [75] [76] [77] [78] [43] [79] [80].”