Recurrent neural-network-based maximum frequency deviation prediction using probability power flow dynamic tool

Sungyoon Song, Yoongun Jung, Changhee Han, Seungmin Jung, Minhan Yoon, Gilsoo Jang

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)182054-182064
Number of pages11
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2020

Keywords

  • Frequency stability
  • Probability power flow
  • RNN
  • Randomness.

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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