TY - GEN
T1 - Machine Learning Basics and Potential Applications in Power Systems
AU - Xue, Tao
AU - Karaagac, Ulas
AU - Kocar, Ilhan
AU - Vavdareh, Masoud Babaei
AU - Ghafouri, Mohsen
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2024/2/27
Y1 - 2024/2/27
N2 - Power system studies have relied on physical model-driven methods for decades. However, uncertainties arising from integrating renewable energies, nonlinearities introduced by power electronic devices, increased dependence on cyber-physical systems, and the need for fast and accurate big data analysis challenge traditional power system methodologies. In recent years, machine learning (ML) has revolutionized scientific research, making it possible to address constantly changing and nonlinear questions without the need for pre-determined models. This paper first introduces the basics of ML and typical algorithms to new researchers and readers. Then typical examples of applying ML to power systems are proposed but not limited to electricity customer clustering, load and electricity price forecasting, power system dynamics prediction, impedance model identification, power system security, optimal load flow, load management control and inverter-based resources (IBR) control. In future studies, it is encouraged to embrace this emerging technology and utilize a combination of data-driven and model-driven methods.
AB - Power system studies have relied on physical model-driven methods for decades. However, uncertainties arising from integrating renewable energies, nonlinearities introduced by power electronic devices, increased dependence on cyber-physical systems, and the need for fast and accurate big data analysis challenge traditional power system methodologies. In recent years, machine learning (ML) has revolutionized scientific research, making it possible to address constantly changing and nonlinear questions without the need for pre-determined models. This paper first introduces the basics of ML and typical algorithms to new researchers and readers. Then typical examples of applying ML to power systems are proposed but not limited to electricity customer clustering, load and electricity price forecasting, power system dynamics prediction, impedance model identification, power system security, optimal load flow, load management control and inverter-based resources (IBR) control. In future studies, it is encouraged to embrace this emerging technology and utilize a combination of data-driven and model-driven methods.
KW - Data-Driven Methods
KW - Deep Learning
KW - Machine Learning
KW - Power Systems
KW - Reinforcement Learning
UR - http://www.scopus.com/inward/record.url?scp=85187219393&partnerID=8YFLogxK
U2 - 10.1109/ICECCE61019.2023.10441935
DO - 10.1109/ICECCE61019.2023.10441935
M3 - Conference article published in proceeding or book
AN - SCOPUS:85187219393
T3 - 4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023
BT - 4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023
Y2 - 30 December 2023 through 31 December 2023
ER -