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The reliability and stability of differential protection in power transformers could be threatened by several types of inferences, including magnetizing inrush currents, current transformer saturation, and overexcitation from external faults. The robustness of deep learning applications employed for power system protection in recent years has offered solutions to deal with several disturbances. This paper presents a method for detecting internal faults in power transformers occurring simultaneously with inrush currents. It involves utilizing a data window (DW) and stacked denoising autoencoders. Unlike the conventional method, the proposed scheme requires no thresholds to discriminate internal faults and inrush currents. The performance of the algorithm was verified using fault data from a typical Korean 154 kV distribution substation. Inrush current variation and internal faults were simulated and generated in PSCAD/EMTDC, considering various parameters that affect an inrush current. The results indicate that the proposed scheme can detect the appearance of internal faults occurring simultaneously with an inrush current. Moreover, it shows promising results compared to the prevailing methods, ensuring the superiority of the proposed method. From sample N–3, the proposed DNN demonstrates accurate discrimination between internal faults and inrush currents, achieving accuracy, sensitivity, and precision values of 100%.

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.

Malfunctions in relay protection devices are predominantly caused by current transformer (CT) saturation which produces distortion in current measurements and disturbances in power system protection. The development of deep learning in power system protection is on the rise recently because of its robustness. This study presents a CT saturation detection where the secondary current becomes distorted. The proposed scheme offers a wide range of saturation detection and consists of a moving-window technique and stacked denoising autoencoders. Moreover, Bayesian optimization was used to minimize the difficulty of determining neural network structure for the proposed approach. The performance of the algorithm was evaluated for a-g faults on 154 kV and 345 kV overhead transmission line in South Korea. The waveform variation has been generated by PSCAD for different scenarios that heavily influence CT saturation. Moreover, a comparative analysis with other methods demonstrated the superiority of the proposed DNN method. With the proposed algorithm to detect CT saturation, it significantly yielded high accuracy and precision for CT saturation detection which were approximately 99.71% and 99.32%, respectively.

We developed an International Electrotechnical Commission (IEC) 61850-based centralized protection scheme to prevent single line-to-ground (SLG) faults in the feeders and busbars of ungrounded distribution systems. Each feeder intelligent electronic device (IED) measures its zero-sequence current and voltage signals and periodically transmits zero-sequence phasors to a central IED via a Generic Oriented Object Substation Event message. Using the zero-sequence phasors, the central IED detects SLG faults in feeders and busbars. To achieve centralized protection, angle differences between the zero-sequence currents and voltage phasors are exploited, and their calculation compensates for data desynchronization. The feeder IEDs were implemented using the MMS-EASE Lite library, while the transmitted zero-sequence phasors were calculated based on fault signals simulated by Power System Computer Aided Design / Electro-Magnetic Transient Design and Control (PSCAD/EMTDC). The central IED determined if the SLG fault was in a feeder or busbar by aggregating and analyzing the zero-sequence phasors received from the feeder IEDs. The results confirmed the validity and efficiency of our centralized protection scheme.