Automatic Voltage Stabilization System for Substation using Deep Learning

Jiyong Moon¹, Minyeong Son², Byeongchan Oh³, Jeongpil Jin⁴ and Younsoon Shin⁵

Department of Business Administration, Dongguk University
30, Pildong-ro 1-gil, Jung-gu, Seoul, Korea
asdwldyd@dongguk.edu
Department of Medical Biotechnology, Dongguk University
30, Pildong-ro 1-gil, Jung-gu, Seoul, Korea
smya0930@dongguk.edu
Department of Statistics, Dongguk University
30, Pildong-ro 1-gil, Jung-gu, Seoul, Korea
oxox97@dongguk.edu
Department of Industrial System Engineering, Dongguk University
30, Pildong-ro 1-gil, Jung-gu, Seoul, Korea
chin9510@dongguk.edu
Department of Computer Science, Dongguk University
30, Pildong-ro 1-gil, Jung-gu, Seoul, Korea
ysshin@dongguk.edu

Abstract

The operating voltage in the substation must be maintained at its rated voltage within the specified standard because a voltage outside the specified range may cause a malfunction of the power facility and interfere with the stable power supply. Therefore, the voltage regulation process to maintain the rated voltage of the substation is essential for the stability of the power system. However, the voltage regulation process is currently performed manually by resident staff. Voltage regulation based on human judgment increases the uncertainty of voltage stabilization and makes efficient operation in consideration of the economic feasibility of power facilities difficult. Therefore, this paper proposes an automatic voltage stabilization system that can automatically perform voltage regulation. Instead of predicting the electrical load or overvoltage conditions studied so far, we focus on more direct, scalable input capacity prediction for an automatic voltage stabilization system. First, the proposed system predicts the input capacity required for a given situation through a trained stacked LSTM model. Second, an optimal regulation plan is derived through an optimization process that considers the economic feasibility of power facility operation. Additionally, the development of the user interface makes it possible to visualize the operation of algorithms and effectively communicate the models’ predictions to the user. Experimental results based on real substation data show that the proposed system can effectively automate the voltage regulation process.

Key words

automatic voltage stabilization system, energy system, input capacity prediction, deep learning, optimal regulation plan

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS220509050M

Publication information

Volume 21, Issue 2 (April 2024)
Special Issue on Deep Learning Techniques in Intelligent Internet of Things and 5G Communication Networks
Year of Publication: 2024
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Moon, J., Son, M., Oh, B., Jin, J., Shin, Y.: Automatic Voltage Stabilization System for Substation using Deep Learning. Computer Science and Information Systems, Vol. 21, No. 2, 437–452. (2024), https://doi.org/10.2298/CSIS220509050M