Offshore wind power has attracted significant attention due to its high potential, capability for large-scale farms, and high capacity factor. However, it faces high investment costs and issues with subsea power transmission. Conventional high-voltage AC (HVAC) methods are limited by charging current, while high-voltage DC (HVDC) methods suffer from the high cost of power conversion stations. The low-frequency AC (LFAC) method mitigates the charging current through low-frequency operation and can reduce power conversion station costs. This paper aims to identify the economically optimal frequency by comparing the investment costs of LFAC systems at various frequencies. The components of LFAC, including transformers, offshore platforms, and cables, exhibit frequency-dependent characteristics. Lower frequencies result in an increased size and volume of transformers, leading to higher investment costs for offshore platforms. In contrast, cable charging currents and losses are proportional to frequency, causing the total cost to reach a minimum at a specific frequency. To determine the optimal frequency, simulations of investment costs for varying capacities and distances were conducted.

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.

Power companies employ comprehensive evaluations to manage facilities, such as transformers, lines, and generators, assessing their status and calculating the probability of failure. Within this asset management framework, a key metric that indicates the condition of facilities is the health index. To develop an optimal investment strategy for facility maintenance and management, a method for accurately calculating the health index is essential. Recent studies have explored the application of machine learning to improve accuracy. However, these studies ignore the risk of misclassification, which can result in significant financial losses when predicting the health index of power transformers. Therefore, this study proposes a health index calculation algorithm that incorporates cost-sensitive learning to assess the impact of each misclassification scenario, construct a cost matrix, and minimize the cost through learning. Input parameters are selected on the basis of the power transformer’s structure, failure mode effect analysis, failure diagnosis techniques, and historical failure cases. During the learning process, the relationship between actual and predicted classes is analyzed, and the risk associated with each misclassification scenario is quantified to describe the cost matrix. The effectiveness of the proposed health index calculation algorithm is validated using in-service power transformer inspection data, demonstrating a reduction in the risk of specific misclassification scenarios.

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).

This paper presents a frequency-dependent cost analysis model for optimizing the economic performance of Low-Frequency AC (LFAC) transmission systems for offshore wind integration. Unlike previous studies that assumed fixed frequencies (e.g., 16.7 Hz or 20 Hz), the proposed model evaluates how variations in frequency affect the cost of key components, including transformers, offshore platforms, and submarine cables. By modeling the cost of the total system as a function of frequency, transmission capacity, and distance, the system’s optimal operating frequency is identified under various conditions. In addition, LFAC, High-Voltage AC (HVAC), and High-Voltage DC (HVDC) systems are compared based on the concept of economic critical distance, which defines the boundary between the most economically favorable transmission technologies depending on distance and capacity conditions. The results show that HVAC is the most cost-effective solution for short transmission distances, LFAC is optimal for medium-range transmission, and HVDC is favorable for long-distance applications. Notably, LFAC outperforms HVDC when cable multiplication is avoided, confirming its competitiveness in midrange scenarios. These findings demonstrate that considering frequency as a design parameter significantly improves the cost efficiency of LFAC systems and should be reflected in the early-stage planning of offshore transmission system design.

The growing scale of offshore wind farms and increasing transmission distances has driven the demand for optimized offshore substation (OSS) configurations. This study proposes a comprehensive techno-economic framework to minimize the total lifecycle cost (LCC) of an OSS by determining the optimal number of OSSs and transformers considering wind farm capacity and transmission distance. The methodology incorporates three cost models: capital expenditure (CAPEX), operational expenditure (OPEX), and expected energy not supplied (EENS). CAPEX considers transformer costs, topside structural mass effects, and nonlinear installation costs. OPEX accounts for substation maintenance and vessel operating expenses, and EENS is calculated using transformer failure probability models and redundancy configurations. The optimization is performed through scenario-based simulations and a net present value (NPV)-based comparative analysis to determine the cost-effective configurations. The quantitative analysis demonstrates that for small- to medium-scale wind farms (500–1000 MW), configurations using 1–2 substations and 3–4 transformers achieve minimal LCC regardless of the transmission distance. In contrast, large-scale wind farms (≥1500 MW) require additional substations to mitigate transmission losses and disruption risks, particularly over long distances. These results demonstrate that OSS design should holistically balance initial investment costs, operational reliability, and supply security, providing practical insights for cost-effective planning of next-generation offshore wind projects.