| Title |
High-resistance Ground Fault Detection through Deep Learning in a Distribution System with Distributed Generation |
| Authors |
박종영(Jong-young Park) ; 이한민(Hanmin Lee) ; 조규정(Gyu-Jung Cho) ; 정호성(Hosung Jung) ; 한문섭(Moonseob Han) |
| DOI |
https://doi.org/10.5370/KIEE.2022.71.11.1715 |
| Keywords |
High-resistance ground fault; Deep learning; CNN; Distribution system; Protection system |
| Abstract |
This paper proposes a method to detect a high-resistance ground fault in a distribution system with complicated configuration such as installation of a distributed generation. A method to detect high-resistance ground fault accidents by converting the fault current into visual data and applying the CNN technique to this is presented and verified. The data for learning the CNN technique was generated through simulation of the model system. Simulations were performed for data generation by changing the fault resistance, the size, location of faults and amount of distributed power generation, in the case of a high-resistance ground fault and an increase in load in the model system. The generated data was transformed into graphic data by applying Morlett wavelet transform, and then learning was performed by applying CNN. As a result of the learning, high-resistance ground faults were identified with 98.29% accuracy, and a protective algorithm including this result that can respond to high-resistance ground faults occurring in the distribution system was proposed. |