@inproceedings{921edb77f95b42d49e00d5910d8cd853,
title = "Development of Automatic Voltage Stabilization System for Substation Using Deep Learning",
abstract = "The voltage adjustment process is currently done manually by resident staff. As such, voltage regulation based on human judgement not only entails great uncertainty about voltage stabilization but also makes efficient operation in consideration of the economic feasibility of power facilities impossible. Therefore, this paper proposes an automatic voltage stabilization system that can automatically perform voltage adjustment. The proposed system predicts the required input capacity, and then predicts the optimal adjustment method considering the efficiency of power facility operation by adding an optimization process. In addition, through the development of UI, it is possible to visualize the operation of the algorithm and effectively communicate the prediction of the model to the user.",
keywords = "Capacity prediction, Voltage stabilization system",
author = "Jiyong Moon and Minyeong Son and Byeongchan Oh and Jeongpil Jin and Kwangil Kim and Younsoon Shin",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 5th International Conference on Innovative Computing, IC 2022 ; Conference date: 19-01-2022 Through 21-01-2022",
year = "2022",
doi = "10.1007/978-981-19-4132-0_14",
language = "English",
isbn = "9789811941313",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "133--134",
editor = "Yan Pei and Jia-Wei Chang and Hung, {Jason C.}",
booktitle = "Innovative Computing - Proceedings of the 5th International Conference on Innovative Computing, IC 2022",
address = "Germany",
}