The robust and efficient diagnosis of power quality disturbances (PQDs) in electric power systems (EPSs) is one of the most important steps to protect a power system with minimal damage. However, the conventional fault detection methods used in the EPS mainly rely on heavy mathematical calculations, resulting in delayed actions against PQDs. To overcome these limitations, deep learning has been recently proposed to diagnose PQDs in the EPS, which allows the extraction of features from a huge amount of data to delineate subtle differences in electrical waveforms under faulty conditions. In this study, a deep learning-based diagnostic method for PQDs was proposed by exploiting a convolutional neural network (CNN) and simulated realistic three-phase voltage and current waveforms obtained from the PSCAD/EMTDC software. Specifically, PQDs related to various faults in EPSs were assessed to demonstrate the applicability of the deep-learning method as a fault diagnostic method. The proposed CNN model, trained by end-to-end learning and supervised learning approaches, successfully classified the type and location of the faults. Moreover, we found that simulated data obtained at the sampling rate of 50 Hz also accurately diagnosed the faults with an accuracy of over 99%; therefore, the proposed method could be a potential diagnostic tool in practice.

A power flow study aims to analyze a power system by obtaining the voltage and phase angle of buses inside the power system. Power flow computation basically uses a numerical method to solve a nonlinear system, which takes a certain amount of time because it may take many iterations to find the final solution. In addition, as the size and complexity of power systems increase, further computational power is required for power system study. Therefore, there have been many attempts to conduct power flow computation with large amounts of data using parallel computing to reduce the computation time. Furthermore, with recent system developments, attempts have been made to increase the speed of parallel computing using graphics processing units (GPU). In this review paper, we summarize issues related to parallel processing in power flow studies and analyze research into the performance of fast power flow computations using parallel computing methods with GPU.

Electric vehicle (EV) demand is considerably increasing, aided by battery technology development and government policy support. Therefore, it is necessary to consider various charging infrastructures and secure sufficient power to support this. In several cities or countries, a large population lives in a small area, and the supply of large residential complexes such as apartments is common. Given that old apartments may not have sufficient power capacity, it is challenging to secure additional power for charging EVs. In particular, power shortages can intensify during high power usage, such as the summer season, and may become an obstacle to the spread of EVs. Therefore, an efficient charging strategy for residential complexes must be established under the condition of limited power supply. This study proposes two scheduling methods to optimally charge EVs in residential complexes considering limited power supply. The proposed scheduling algorithms are thoroughly examined through extensive simulations with parking data collected from real situations and are demonstrated to be effective for online EV charging systems.

We consider channel estimation specific to turbo equalization for multiple-input multiple-output (MIMO) wireless communication. We develop soft-decision-driven sequential algorithms geared to a specific pipelined turbo equalizer architecture operating on orthogonal frequency division multiplexing (OFDM) symbols. One interesting feature of the pipelined turbo equalizer is that multiple soft-decisions become available at various processing stages. A tricky issue is the fact that these multiple decisions from different pipeline stages have correlated decision errors as well as varying levels of reliability. This paper establishes an optimization strategy for the channel estimator to track the target channel while dealing with observation sets with different qualities. The resulting algorithm is basically a linear sequential estimation algorithm and, as such, is Kalman-like in nature. The main difference here, however, is that the proposed algorithm must deal with the inherent correlation that exist among the multiple module outputs that cannot easily be removed by the traditional innovation approach. The proposed algorithm continuously monitor the quality of the feedback decisions and incorporate it in the channel estimation process. The proposed channel estimation schemes show certain performance and complexity advantages over existing EM-based algorithms.

Thanks to technological advances, the number of large-scale ESS applications is increasing in power systems. Therefore, the need for an ESS model to analyze the effect through simulation has also increased. In this study, an ESS analysis model was developed for transmission-level power-system analysis. The developed model has a relatively uncomplicated configuration compared to the previous model. The ESS model was developed in a form that can be exploited in the PSS^®E, commercial power-system analysis program. The programming language FORTRAN was used for model development. A detailed process and necessary data for the model development are described. The operation of the developed ESS model was verified through simulation using two test systems. The results of this study can be used for power-system analysis and as reference material for model development.