Distribution system state estimation through Gaussian mixture model of the load as pseudo-measurement

By Ravindra Singh, Bikash Chandra Pal, Rabih A. Jabr. 2010

208 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/8193418abb992bfb70e3e077eb7ab9cab7cf209a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Jianhui Wang
2. Bikash Chandra Pal

In Electrical and Electronic Engineering

1. Zhaoyu Wang
2. Jianhui Wang
3. Bikash Chandra Pal
4. Goran Strbac
5. Efthymios Manitsas
6. Andrija T. Saric
7. Ravindra Singh
8. Vladimir Terzija

Citations in the corpus

• A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems (2018, methodology): “Expectation Maximization (EM) [46] [47], time-varying variance and mean modeling [44], correlation analysis (between total and individual consumption) [48], nodal active-reactive correlation analysis [15], internodal and intranodal correlation modeling [16], intertemporal correlation analysis [6], m”
• Distribution System State Estimation Using an Artificial Neural Network Approach for Pseudo Measurement Modeling (2012, methodology): “If several components have comparable marginal densities, they can be merged together using the mixture reduction technique described in [13].”
• Stochastic Monitoring of Distribution Networks Including Correlated Input Variables (2013, background): “The representation of power demand as non-Gaussian variables for state estimation purposes was also presented in [10].”