Version 1
: Received: 5 April 2024 / Approved: 5 April 2024 / Online: 5 April 2024 (13:59:54 CEST)
How to cite:
Hallmann, M.; Pietracho, R.; Komarnicki, P. Comparison of Artificial Intelligence and Machine LearningMethods used in Electric Power System Operation. Preprints2024, 2024040445. https://doi.org/10.20944/preprints202404.0445.v1
Hallmann, M.; Pietracho, R.; Komarnicki, P. Comparison of Artificial Intelligence and Machine LearningMethods used in Electric Power System Operation. Preprints 2024, 2024040445. https://doi.org/10.20944/preprints202404.0445.v1
Hallmann, M.; Pietracho, R.; Komarnicki, P. Comparison of Artificial Intelligence and Machine LearningMethods used in Electric Power System Operation. Preprints2024, 2024040445. https://doi.org/10.20944/preprints202404.0445.v1
APA Style
Hallmann, M., Pietracho, R., & Komarnicki, P. (2024). Comparison of Artificial Intelligence and Machine LearningMethods used in Electric Power System Operation. Preprints. https://doi.org/10.20944/preprints202404.0445.v1
Chicago/Turabian Style
Hallmann, M., Robert Pietracho and Przemlyslaw Komarnicki. 2024 "Comparison of Artificial Intelligence and Machine LearningMethods used in Electric Power System Operation" Preprints. https://doi.org/10.20944/preprints202404.0445.v1
Abstract
The methods of Artificial Intelligence (AI) have been used in the planning and operation of power systems for more than 40 years. In recent years, due to the development of microprocessor and data storage technologies, the effectiveness of this use has greatly increased. This paper provides a systematic overview of the application of AI including the use of Machine Learning (ML) to the power system. The potential application areas are divided into four blocks and the classification matrix has been used for clustering the AI application tasks. Furthermore, the data acquisition methods for setting the parameters of AI and ML algorithms are presented and discussed in a systematic way considering the supervised and unsupervised learning methods. Based on this, three complex application examples: wind power generation forecasting, smart grid security as-sessment (using two methods), and automatic system fault detection are presented and discussed in detail. Summary and outlook conclude the paper.
Keywords
Electric power system; smart grid; artificial intelligence; machine learning; digitalization; sector coupling
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
