Neural network based multi-dimensional feature forecasting for bad data detection and feature restoration in power systems

This paper deals with new methods for bad data detection and restoration, which can help to improve modern state estimation. Special interest is focused on the detection of bad data and outliers in the measurements from the power system. The new artificial neural network based method involves both the time series of the measured data and their physical correlation. The neural network (NN) inputs are power system measurements from the history, e.g. 5-30 minutes back. Then, the NN is utilized to predict the expected on-line measurements. When the measured data show significant differences to the NN prediction, the measurements are suspicious to contain bad data. Finally, identified bad data inputs will be restored by NN based optimization to their correct value. This paper shows that NNs implemented for bad data detection and restoration are a valuable addition for future state estimation