A Weighted Deep Neural Network for Processing Measurements for State Estimation

Processing of power system data containing outliers and noise is important for state estimation. This article aims to improve the quality of data to the state estimator. It addresses noises in the data, viz., normally distributed noise and bias. Along with this, it also handles the outliers, missing data, and time stamping error. In the first stage, outliers, missing data, and time stamping error are handled. In the second stage, data from the first stage pass through the proposed weighted deep neural network that makes use of measurement variance information to reduce noises and bias present in the data. The data after noise reduction are utilized by the state estimation program to find the system states. The proposed method is tested on the IEEE 13-node test feeder.