LSTM-Based Fault Direction Estimation and Protection Coordination for Networked Distribution System

IEEE ACCESS(2022)

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摘要
While the world's power distribution system resembles an intricate web-like structure, the most conventionally implemented distribution mechanism is the radial distribution system (RDS) where connection points for each distribution line are normally kept open. However, disadvantages regarding the traditional system have led to active research on establishing the networked distribution system (NDS), in which multiple circuits are interconnected with electricity as well as high-speed communication systems. The NDS offers multiple advantages including increased facility utilization, increased hosting capacity, and higher terminal voltage. Conversely, an unsolved issue prevails as the existing protection coordination method designed for the RDS is inadequate for fault occurrences in the NDS due to inaccurate fault direction identification. Hence, there is an urgent need for an alternative protection coordination method that allows high precision fault direction identification through communication. Moreover, the application of various existing technologies is hindered as the distance relay protection coordination algorithm malfunctions in situations where distribution lines are of short length, loads are dispersed onto multiple lines, and integration of distributed generation (DG) is frequent. Therefore, this paper presents a fault direction identification method that uses the waveform of the fault current based on long short-term memory (LSTM) neural network and a communication-based protection coordination scheme that can be applied in fault situations within an NDS.
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关键词
Circuit faults, Fault diagnosis, Circuit breakers, Fault currents, Fault detection, Torque, Protective relaying, Protection coordination, distribution system, closed-loop system, networked distribution system, fault direction, long short-term memory, deep learning neural network
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