Overhead lines that are exposed to the outdoors are susceptible to faults such as open conductors on weak points and disconnection caused by external factors such as typhoons. Arcs that occur during disconnection generate energy at a high heat of over 10,000 °C, requiring swift fault shut-off. However, most conventional fault detection methods to protect electrical power systems detect an overcurrent; thus, they can only detect faults after the line is disconnected and the cross-section of the line that generates the arc discharge makes contact with another line or the ground, causing a high risk of fire. Furthermore, in the case of ground faults owing to the disconnection of overhead lines, the load and the grounding impedance are not parallel. Therefore, in the case of the fault current not exceeding the threshold or a high impedance fault due to the high grounding impedance of the surrounding environment, such as grass or trees, it is difficult to determine overhead line faults with conventional fault detection methods. To solve these issues, this paper proposes an AI-based open conductor fault detection method on overhead lines that can clear the fault before the falling open conductor line comes into contact with the ground’s surface so as to prevent fire. The falling time according to the height and span of the overhead line was calculated using a falling conductor model for the overhead line, to which the pendulum motion was applied. The optimal input data cycle that enables fault detection before a line–ground fault occurs was derived. For artificial intelligence learning to prevent wildfires, the voltage and current signals were collected through a total of 432 fault simulations and were wavelet-transformed with a deep neural network to verify the method. The proposed total scheme was simulated and verified with MATLAB.

Recently, because of the increase in the number of connections to Distributed Generation (DG), the problem of lowering voltage stability in the distribution system has become an issue. Reactive power compensators, such as Static Synchronous Compensators (STATCOM), may be used to solve the problem of voltage stability degradation. However, because of the complexity of the distribution system, it is very difficult to select the installation location for STATCOM. Furthermore, when installed in the wrong location, economical efficiency and availability problems may occur. This paper proposes a Virtual STATCOM Configuration and Control method that would operate like a single STATCOM based on multiple DGs connected to the system. The proposed Virtual STATCOM has the merit of being economical by using existing facilities without adding new power facilities, and it solves the problem of the difficulty of selecting the installation location because of the complexity of the distribution system. In addition, while the conventional STATCOM uses an independent control method in consideration of the power quality of the access point, the Virtual STATCOM performs the Point of Common Coupling (PCC) power quality compensation using the integrated control of multiple DGs connected to the system. In the proposed method, the Virtual STATCOM integrated control algorithm is configured by adopting linear programming, and the compensation is performed while considering the distance between DG and PCC, the inverter’s rated capacity, and the power generation. The performance of the Virtual STATCOM power quality compensation was verified using MATLAB/SIMULINK and Real Time Simulator (OPAL-RT).

Data sparsity, particularly the partial loss of diagnostic data caused by sensor failures, transmission errors, or missed inspections, frequently occurs in practical power transformer operations and significantly degrades the accuracy and reliability of health index (HI) assessments. In this study, a machine learning-based HI evaluation framework is developed using key diagnostic input parameters systematically derived from failure mode and effect analysis (FMEA) and established transformer diagnostic practices. To compensate for missing data, an unsupervised autoencoder (AE)-based imputation method is introduced and benchmarked against conventional statistical supplementation techniques, namely mean and mode imputation. The experimental results, obtained using real inspection-based transformer diagnostic records, demonstrate that the AE-based approach effectively preserves inter-variable correlations and latent data structures by learning nonlinear feature relationships. As a result, the proposed method maintains robust and consistent HI classification performance under varying missing-data conditions. Furthermore, validation using confirmed transformer failure cases shows that the AE method more accurately reconstructs missing dissolved gas analysis indicators and improves the identification of high-risk equipment compared with statistical imputation. Overall, the proposed approach provides decision-consistent HI evaluations even when diagnostic data are incomplete, thereby reducing uncertainty in maintenance planning and minimizing the need for additional follow-up inspections solely to compensate for missing information.

Renewable energy sources continue to increase due to the energy transition; thus, the generation output of conventional power sources is decreasing. The installation of renewable energy sources can lead to the concentration of these sources depending on geographical characteristics, which may cause bottlenecks between the renewable energy generation sites and load centers, and such bottlenecks can result in power shortages in load centers and may cause issues that limit the integration of renewable energy. Thus, this paper proposes the application of an LFAC multiport asynchronous grid connection system to solve these problems, where the frequency conversion device uses a variable frequency transformer (VFT). In addition, the installation location of the proposed LFAC multiport asynchronous grid connection system is selected using a grid partitioning technique, and the optimal power flow is performed to minimize the generation costs. The grid partitioning was conducted using the IEEE 39 bus system, and the feasibility of the proposed LFAC multiport asynchronous grid connection system was verified through simulation tests of the generation and load demands in the grid. In addition, the VFT control and optimal power flow control performances were confirmed through MATLAB/Simulink.

Conventional time-based inspection of power transformers does not reflect differences in the condition and risk of individual units when inspection intervals and items are set. This paper proposes a risk-based inspection planning framework that determines the inspection intervals and items of 154 kV power transformers using statistical risk curves and a mapping between failure modes and inspection items derived from failure mode and effects analysis (FMEA). The asset-level probability of failure is obtained from a two-parameter Weibull distribution and apportioned to the winding, bushing, and on-load tap changer (OLTC) according to their failure contributions, and the consequence of failure is evaluated in cost terms for the reliability, financial, safety, and environmental aspects. The risk curve is updated with inspection results; the next inspection time is the year in which the updated risk reaches the next reference risk, and the inspection items follow a grade based on the annual risk increment. Transformers selected in the same year are prioritized by transformer-level risk within the available budget. A scenario-based case study of five transformers analyzes how the inspection times and items change under different condition-change settings and compares the number of inspections, total inspection cost, and present value with time-based inspection. The inspection assignments and unassigned unit-years of the two approaches are also compared under the same budget constraint.