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).
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.
There is growing interest in grid connections between different micro grids, countries, and the selection of interconnection method should be based on an economic analysis. This paper analyses the economics of VFT (Variable Frequency Transformer) and HVDC (High Voltage DC) and proposes an optimal grid interconnection method. The economic evaluation was conducted based on the new investment cost depending on the capacity of the facilities needed for the grid connection, and the feasibility study was carried out to find out which method is more advantageous economically.
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