Very Short-Term Power System Frequency Forecasting

Yurdakul O., Eser F., Sivrikaya F., Albayrak S.

IEEE ACCESS, vol.8, pp.141234-141245, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 8
  • Publication Date: 2020
  • Doi Number: 10.1109/access.2020.3013165
  • Journal Name: IEEE ACCESS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Page Numbers: pp.141234-141245
  • Keywords: Power systems, Forecasting, Time-frequency analysis, Frequency response, Generators, Tools, Forecasting, frequency control, frequency response, long short-term memory (LSTM), recurrent neural network (RNN)
  • Middle East Technical University Affiliated: Yes


Power system frequency plays a pivotal role in ensuring the security, adequacy, and integrity of a power system. While some frequency response services are automatically delivered to maintain the frequency within the stipulated limits, certain cases may require that system operators (SOs) manually intervene-against the clock-to take the necessary preventive or corrective actions. As such, SOs can be greatly aided by practical tools that afford them greater temporal leeway. To this end, we propose a methodology to forecast the power system frequency in the subsequent minute. We perform an extensive analysis so as to identify the factors that influence power system frequency. By effectively exploiting the identified factors, we develop a forecasting methodology that harnesses the long short-term memory model. We demonstrate the effectiveness of the proposed methodology on Great Britain transmission system frequency data using comparative assessments with selected benchmarks based on various evaluation metrics.