IEEE Power Electronics Magazine - March 2021 - 38

applications of machine learning for power electronics
and smart grid.
Amin Y. Fard received his B.Sc. degree from Azarbaijan
Shahid Madani University, Tabriz, Iran in 2011, and obtained
his M.Sc. degree from University of Tabriz, Tabriz, Iran in
2014, both in Electrical Power Engineering. He was a Ph.D.
student at K-State University from 2018-2020. Since 2020, he
is pursuing the Ph.D. degree in Electrical and Computer
Engineering Department at University of Illinois at Chicago,
IL, United States.
Muhammad Farooq Umar received the B.Sc. degree
in Electrical engineering from Lahore University of Management Sciences, Pakistan, in 2014. He has done Masters in
Electrical Power Engineering from Department of Electrical
Power Engineering, U.S. Pakistan Center for Advanced
Studies in Energy, National University of Sciences and Technology, Pakistan in 2018. He was a Ph.D. student in Department of Electrical & Computer Engineering at Kansas State
University from 2019-2020. Since 2020, he is pursuing the
Ph.D. degree in Electrical and Computer Engineering
Department at University of Illinois at Chicago, IL, United
States. Currently, he is Fulbright Grantee 2019.
Mohsen Hosseinzadehtaher received his M.Sc.
degree in electrical engineering from Amirkabir University
of Technology, Tehran, Iran, in 2014. From 2018 to 2020, he
was a Ph.D. student in Electrical and Computer Engineering
Department at Kansas State University, Manhattan, United
States. Since 2020, he is pursuing the Ph.D. degree in Electrical and Computer Engineering Department at University
of Illinois at Chicago, IL, United States. He received the best
paper award in the IEEE SGRE in 2019.
Mohammad B. Shadmand (shadmand@uic.edu)
received the Ph.D. degree in electrical engineering from
Texas A&M University, College Station, TX, United States in
2015. From 2015 to 2017, he was a research engineer and
instructor at Texas A&M University, College Station, United
States. From 2017 to 2020, he was an assistant professor
with the Department of Electrical and Computer Engineering, Kansas State University, Manhattan, United States. Since
2020, he is an assistant professor and director of Intelligent
Power Electronics at Grid Edge Research Laboratory in the
Department of Electrical and Computer Engineering at University of Illinois at Chicago, United States. His current
research interests include artificial intelligence for power
electronics systems, grid-tied power electronics interfaces
with advance functionalities, grid situational awareness, and
control of smart microgrid systems. He has served as Technical Program Cochair of the 2019 IEEE Smart Grid & Renewable Energy Conference. He serves as an associate editor of
IEEE Transactions on Industrial Electronics, IEEE Transactions on Industry Applications, and IET Renewable Power
Generation. He is a senior member of IEEE.

solutions, " IEEE Trans. Ind. Appl., vol. 55, no. 6, pp. 7657-7670, 2019. doi:
10.1109/TIA.2019.2936788.
[2] S. Ramachandran, " Applying AI in power electronics for renewable
energy systems [expert view], " IEEE Power Electron. Mag., vol. 7, no. 3, pp.
66-67, 2020. doi: 10.1109/MPEL.2020.3012009.
[3] J. Gareth, W. Daniela, H. Trevor, and T. Robert, An Introduction to Statistical Learning: With Applications in R. Berlin: Springer-Verlag, 2013.
[4] S. Zhao, F. Blaabjerg, and H. Wang, " An overview of artificial intelligence
applications for power electronics, " 2020.
[5] T. Dragicˇevic´, P. Wheeler, and F. Blaabjerg, " Artificial intelligence aided automated design for reliability of power electronic systems, " IEEE Trans. Power
Electron., vol. 34, no. 8, pp. 7161-7171, 2018. doi: 10.1109/TPEL.2018.2883947.
[6] H. Momeni et al., " Fault diagnosis in photovoltaic arrays using GBSSL
method and proposing a fault correction system, " IEEE Trans. Ind. Informat., vol. 16, no. 8, pp. 5300-5308, 2019. doi: 10.1109/TII.2019.2908992.
[7] J.-M. Huang, R.-J. Wai, and G.-J. Yang, " Design of hybrid artificial bee
colony algorithm and semi-supervised extreme learning machine for PV fault
diagnoses by considering dust impact, " IEEE Trans. Power Electron., vol.
35, no. 7, pp. 7086-7099, 2019. doi: 10.1109/TPEL.2019.2956812.
[8] V. Le, X. Yao, C. Miller, and B.-H. Tsao, " Series DC arc fault detection
based on ensemble machine learning, " IEEE Trans. Power Electron., vol. 35,
no. 8, pp. 7826-7839, 2020. doi: 10.1109/TPEL.2020.2969561.
[9] D. Cao et al., " A multi-agent deep reinforcement learning based voltage
regulation using coordinated PV inverters, " IEEE Trans. Power Syst., vol.
35, no. 5, pp. 4120-4123, 2020. doi: 10.1109/TPWRS.2020.3000652.
[10] S. Wang et al., " Machine learning based operating region extension of
modular multilevel converters under unbalanced grid faults, " IEEE Trans.
Ind. Electron., pp. 1-1, 2020.
[11] K. Tornai et al., " Classification for consumption data in smart grid based
on forecasting time series, " Electric Power Syst. Res., vol. 141, pp. 191-201,
2016. doi: 10.1016/j.epsr.2016.07.018.
[12] A. Sheikhi, M. Rayati, and A. Ranjbar, " Dynamic load management for
a residential customer; reinforcement learning approach, " Sustain. Cities
Soc., vol. 24, pp. 42-51, 2016. doi: 10.1016/j.scs.2016.04.001.
[13] T. A. Nakabi and P. Toivanen, " An ANN-based model for learning individual customer behavior in response to electricity prices, " Sustain. Energy,
Grids Netw., vol. 18, pp. 100,212, 2019. doi: 10.1016/j.segan.2019.100212.
[14] J. Vermaak and E. Botha, " Recurrent neural networks for short-term
load forecasting, " IEEE Trans. Power Syst., vol. 13, no. 1, pp. 126-132, 1998.
doi: 10.1109/59.651623.
[15] H. Jiang et al., " A short-term and high-resolution distribution system
load forecasting approach using support vector regression with hybrid
parameters optimization, " IEEE Trans. Smart Grid, vol. 9, no. 4, pp. 3341-
3350, 2016. doi: 10.1109/TSG.2016.2628061.
[16] M. Hosseinzadehtaher et al., " Anomaly detection in distribution power
system based on a condition monitoring vector and ultra-short demand forecasting, " Proc. IEEE CyberPELS, Miami, FL, 2020.
[17] X. Li et al., " Securing smart grid: Cyber attacks, countermeasures,
and challenges, " IEEE Commun. Mag., vol. 50, no. 8, pp. 38-45, 2012. doi:
10.1109/MCOM.2012.6257525.
[18] S. R. Snapp et al., " DIDS (distributed intrusion detection system)-motivation, architecture, and an early prototype, " 1991.
[19] A. Abbaspour et al., " Resilient control design for load frequency control

References

system under false data injection attacks, " IEEE Trans. Ind. Electron., vol.

[1] Q. Peng, Q. Jiang, Y. Yang, T. Liu, H. Wang, and F. Blaabjerg, " On the

67, no. 9, pp. 7951-7962, 2020. doi: 10.1109/TIE.2019.2944091.

stability of power electronics-dominated systems: Challenges and potential



38	

IEEE POWER ELECTRONICS MAGAZINE	

z	March 2021
IEEE Power Electronics Magazine - March 2021

Table of Contents for the Digital Edition of IEEE Power Electronics Magazine - March 2021
