Bayesian Deep Neural Network to Compensate for Current Transformer Saturation

Abstract

Current transformer saturation has a negative effect on the operation of IEDs, resulting in their malfunction. Here, we present a technique to compensate for saturated waveforms using Bayesian Deep Neural Network (BDNN) comprising Deep Neural Network (DNN) and Bayesian optimization (BO). DNN, that utilizes stacked denoising autoencoder (SDAE) and Backpropagation (BP), is employed to optimize deep learning structure. Unlike the conventional neural network, which is a shallow network or random-initialize weights, the SDAE calculates optimal weights for each hidden layer and BP uses them to fine-tune which yields results with high performance for CT saturation compensation. To improve the empirical search of training hyperparameters, Bayesian optimization is adopted to decide training-related vectors such as batch size, learning rate, and number of neurons. Finally, the performance of the proposed approach was evaluated on an overhead transmission line which is imported from PSCAD/EMTDC with the different scenarios of fault inception angle, remnant flux, and voltage system. Therefore, numerical cases of saturation were comprehensively evaluated to demonstrate the performance of the proposed algorithm. A comparative analysis was shown to demonstrate that the proposed BDNN is superior to artificial neural network (ANN), and least square error (LES) technique.

Keywords

• Current transformer
• saturation
• deep learning
• stacked denoising auto-encoders
• Bayesian