논문 해외 국제전문학술지(SCI급) Recurrent Neural-Network-Based Maximum Frequency Deviation Prediction Using Probability Power Flow Dynamic Tool
- 학술지 구분 국제전문학술지(SCI급)
- 게재년월 2020-10
- 저자명 정승민,Song, S (Song, Sungyoon),Jung, Y (Jung, Yoongun),Han, C (Han, Changhee),Yoon, M (Yoon, Minhan),Jang, G (Jang, Gilsoo)
- 학술지명 IEEE ACCESS
- 발행처명 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- 발행국가 해외
- 논문언어 외국어
- 전체저자수 6
홈페이지논문 초록 (Abstract)
This paper proposes a recurrent neural network (RNN)-based maximum frequency deviation forecasting model for power systems with high photovoltaic power (PV) penetration. The proposed RNN model extracts the nonlinear features and invariant structures exhibited in regional PV power output data and time-variable frequency data in case of contingency. To capture the regularity and random characteristics of PV power output, a probability power flow-dynamic tool (PPDT) for uncertain power system modeling has been developed. This tool considers all possible combinations of PV power generation patterns, even those with low probability, such as those caused by passing clouds. The results are verified by a comparison of various artificial intelligence methods using case studies from the South Korean power system. An online dispatch algorithm that considers the frequency constraints for a designated contingency can be implemented by using the proposed model.