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

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.Index Terms-Artificial neural networks, distribution system state estimation, Gaussian mixture model, pseudo measurement modeling.

Distribution system state estimation-A step towards smart grid

“…A detailed review on the application of ANNs for load forecasting can be found in [94]. In [95], an ANN-based load forecasting model is presented in which pseudo measurements are generated for DSSE.…”

Two-Stage Linear Three-Phase State Estimation Formulation for Active Distribution Network

“…[5] choose the magnitude and phase angle of the branch current as the state variables with nonlinear measurement equations. The non-negligible feature of distribute SE is that pseudo measurement is necessary to assure observability of distribution network [6]. Pseudo measurements from energy meter and real-time measurements belong to two time scale.…”

Pathway to cost-efficient state estimation of future distribution networks

“…Computation intelligence methods have been investigated for DSSE. For instance, artificial neural network has been adopted to generate pseudo measurements based on a few real measurements in conjunction with typical load profiles [44]. In [45], machine learning methodologies have been investigated to provide reliable input information to a robust state estimation algorithm.…”