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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.

Current transformer saturation is a key issue for power systems because it negatively affects the operation of relays, resulting in the malfunction of protection devices. Recently, deep learning has been used in many academic fields with promising results. Here, we present a technique to compensate for saturated waveforms using deep learning to reconstruct an undistorted waveform. The optimal structure was obtained using pre- and fine-tuning mechanisms, which yielded good performance and initialized the optimum weight during the pre-training stage. The deep learning parameters were determined using particle swarm optimization prior to training. Finally, deep learning performance was evaluated using newly introduced conditions that were not observed during the training stage.

Current Transformer (CT) saturation is regarded as one of the major problems in power system field due to the reason that it negatively impacts the operation of relays, resulting in malfunction protective devices. Recently, deep learning methods have been commonly implemented in most academic fields as the reason of significant generated results. This paper presents a compensation method for saturated waveform by applying deep learning to the aforementioned problem. To achieve a good network structure, pre-training and fine-tuning mechanism have been implemented because it shows a great performance as it well initializes the optimal weight in the pre-training stage. Finally, a training model is evaluated by the newly-introduced conditions, in which has never been experienced during the training stage.