Information Length Quantification and Forecasting of Power Systems Kinetic Energy

Harold R. Chamorro, Adrian Guel-Cortez, Eun-jin Kim, Francisco Gonzalez-Longat, Alvaro Ortega, Wilmar Martinez

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
130 Downloads (Pure)

Abstract

One of the short-coming challenges of power systems operation and planning is the difficulty to quantify the variability of power systems Kinetic Energy (KE) to unveil online additional information for the system operators' decisions support. KE monitoring requires innovative methods to analyse the continuous fluctuations in the KE power's systems. In this paper, we propose the use of information theory, specifically the concept of Information Length (IL), as a way to provide useful insights into the power system KE variability and to demonstrate its utility as a starting point in decision making for power systems management. The proposed IL metric is applied to monthly collected data from the Nordic Power System during three consecutive years in order to investigate the KE evolution. Our results reveal that the proposed method provides an effective description of the seasonal statistical variability enabling the identification of the particular month and day that have the least and the most KE variability. Additionally, by applying a Long Short-Term Memory (LSTM) neural network model to estimate the value of the IL on-line, we also show the possibility of using the metric as data-driven support.

Original languageEnglish
Pages (from-to)4473-4484
Number of pages12
JournalIEEE Transactions on Power Systems
Volume37
Issue number6
Early online date27 Jan 2022
DOIs
Publication statusPublished - 1 Nov 2022

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Keywords

  • Power systems
  • Forecasting
  • Entropy
  • Measurement
  • Fluctuations
  • Wind power generation
  • Probability density function

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