Distribution System State Estimation Using an Artificial Neural Network Approach for Pseudo Measurement Modeling

By Efthymios Manitsas, Ravindra Singh, Bikash Chandra Pal, Goran Strbac. IEEE Transactions on Power Systems, 2012

342 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/5c9cef8c64e8ae971f98911d6a1077d77a7079a9

How researchers see this paper

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In Electrical and Electronic Engineering

1. Jianhui Wang
2. Rabih A. Jabr
3. Zhaoyu Wang
4. Izudin Dzafic
5. Bikash Chandra Pal
6. Yuxuan Yuan
7. Kaveh Dehghanpour
8. Fankun Bu

Citations in the corpus

• Real Time Estimation of Loads in Radial and Unsymmetrical Three-Phase Distribution Networks (2013, methodology): “Pseudo-measurement modeling has been recently discussed in [15], where the computations were based on an artificial neural network in conjunction with load profiles.”
• Sequential Covariance-Matrix Estimation with Application to Mitigating Catastrophic Forgetting (2015, background): “Instead, to avoid complexity, it is often assumes that the covariance matrix is known in advance [6] or that it is restricted to a specific model, such as a diagonal matrix [7], [8].”
• A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems (2018, methodology): “Probabilistic Neural Networks (PNNs) [51], Artificial Neural Network (ANN) [52], clustering algorithms [53], Parallel Distributed Processing networks (PDP) [17], and Nonlinear Auto-Regressive eXogenous (NARX) [18].”