Statistical Representation of Distribution System Loads Using Gaussian Mixture Model

By Ravindra Singh, Bikash Chandra Pal, Rabih A. Jabr. IEEE Transactions on Power Systems, 2010

371 citations recorded by the source corpus.

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.

Subfields: Electrical and Electronic Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/5a93c33e906439be4a62a21ed4610f277e840435

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Citations in the corpus

Statistical Representation of Wind Power Ramps Using a Generalized Gaussian Mixture Model (2018, methodology): “[14] represented all irregular PDFs of load using GMM in various distribution system applications.”
Evaluation of time series techniques to characterise domestic electricity demand (2013, background): “Each profile is characterised by three moments: mean; variance; and aweight coefficient that describe each probability distribution [13].”
A Novel Probabilistic Optimal Power Flow Model With Uncertain Wind Power Generation Described by Customized Gaussian Mixture Model (2016, positive): “An innovative Gaussian mixture model (GMM) has been proposed to represent the pdf of WPG so that the probabilistic load flow (PLF) calculation can be analytically accomplished based on the parameters (coefficient, mean, and variance) of all Gaussian-Functions (GFs) [29], [30], and the expectation ma”
Chance-Constrained Economic Dispatch With Non-Gaussian Correlated Wind Power Uncertainty (2017, methodology): “A standard guideline using Expectation Maximization (EM) algorithm to estimate Γ is provided in [29].”
A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems (2018, neutral): “Expectation Maximization (EM) [46], [47], time-varying variance and mean modeling [44], correlation analysis (between total and individual consumption) [48], nodal active-reactive”
Distribution System State Estimation Using an Artificial Neural Network Approach for Pseudo Measurement Modeling (2012, background): “Details on the GMM formulation for load modeling in distribution systems can be found in [22].”
Stochastic Monitoring of Distribution Networks Including Correlated Input Variables (2013, methodology): “Reference [14] introduces the expectation maximization (EM) algorithm to determine the parameters of GMMs to model PDFs of power demands.”