Title :
Distribution System State Estimation Using an Artificial Neural Network Approach for Pseudo Measurement Modeling
Author :
Manitsas, Efthymios ; Singh, Ravindra ; Pal, Bikash C. ; Strbac, Goran
Author_Institution :
Dept. of Electr. & Electron. Eng., Imperial Coll. London, London, UK
Abstract :
This paper presents an alternative approach to pseudo measurement modeling in the context of distribution system state estimation (DSSE). In the proposed approach, pseudo measurements are generated from a few real measurements using artificial neural networks (ANNs) in conjunction with typical load profiles. The error associated with the generated pseudo measurements is made suitable for use in the weighted least squares (WLS) state estimation by decomposition into several components through the Gaussian mixture model (GMM). The effect of ANN-based pseudo measurement modeling on the quality of state estimation is demonstrated on a 95-bus section of the U.K. generic distribution system (UKGDS) model.
Keywords :
Gaussian processes; distribution networks; least squares approximations; neural nets; power engineering computing; power system measurement; power system state estimation; ANN; DSSE; GMM; Gaussian mixture model; UK generic distribution system model; UKGDS model; WLS state estimation; artificial neural network approach; distribution system state estimation; load profile; pseudomeasurement modeling; weighted least square state estimation; Artificial neural networks; Gaussian mixture model; Load modeling; Measurement uncertainty; Power measurement; Reactive power; State estimation; Artificial neural networks; Gaussian mixture model; distribution system state estimation; pseudo measurement modeling;
Journal_Title :
Power Systems, IEEE Transactions on
DOI :
10.1109/TPWRS.2012.2187804
