Online Learning Control for Harmonics Reduction Based on Current Controlled Voltage Source Power Inverters

Naresh Malla; Ujjwol Tamrakar; Dipesh Shrestha; Zhen Ni; Reinaldo Tonkoski

doi:10.1109/JAS.2017.7510541

Volume 4 Issue 3

Jul. 2017

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2017 > 4(3): 447-457

Naresh Malla, Ujjwol Tamrakar, Dipesh Shrestha, Zhen Ni and Reinaldo Tonkoski, "Online Learning Control for Harmonics Reduction Based on Current Controlled Voltage Source Power Inverters," IEEE/CAA J. Autom. Sinica, vol. 4, no. 3, pp. 447-457, July 2017. doi: 10.1109/JAS.2017.7510541

Citation:

Naresh Malla, Ujjwol Tamrakar, Dipesh Shrestha, Zhen Ni and Reinaldo Tonkoski, "Online Learning Control for Harmonics Reduction Based on Current Controlled Voltage Source Power Inverters," IEEE/CAA J. Autom. Sinica, vol. 4, no. 3, pp. 447-457, July 2017. doi: 10.1109/JAS.2017.7510541

Citation:

Naresh Malla, Ujjwol Tamrakar, Dipesh Shrestha, Zhen Ni and Reinaldo Tonkoski, "Online Learning Control for Harmonics Reduction Based on Current Controlled Voltage Source Power Inverters," IEEE/CAA J. Autom. Sinica, vol. 4, no. 3, pp. 447-457, July 2017. doi: 10.1109/JAS.2017.7510541

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Online Learning Control for Harmonics Reduction Based on Current Controlled Voltage Source Power Inverters

doi: 10.1109/JAS.2017.7510541

Electrical Engineering and Computer Science Department, South Dakota State University, Brookings, SD 57007, USA

Funds: This work was partly supported by Microsoft Inc.Recommended by Associate Editor Qinglai Wei

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Author Bio:
Naresh Malla (S'16) received the B.E. degree from the Department of Electrical Engineering, Pulchowk Campus, Kathmandu, Nepal, in 2010. He is now pursuing the M.S. degree in the Electrical Engineering and Computer Science Department, South Dakota State University, Brookings, SD, USA. His current research interests include machine learning, computational intelligence, adaptive control for power electronic devices, smart grid, resilience of electric power delivery systems, electric power systems stability and control.(e-mail:naresh.malla@jacks.sdstate.edu)

Ujjwol Tamrakar (S'14) received the B.E. degree in electrical engineering, in 2011, from Tribhuvan University, Nepal, and the M.S. degree in electrical engineering, in 2015, from South Dakota State University (SDSU), Brookings, SD, USA. Presently, he is pursuing the Ph.D. degree in electrical engineering from SDSU. His research interests include power electronics and control, grid integration of renewable energy systems, power system stability and power quality.(e-mail:ujjwol.tamrakar@jacks.sdstate.edu)

Dipesh Shrestha (S'16) received the bachelor degree in electrical engineering from Institute of Engineering, Tribhuvan University, Kirtipur, Nepal, the M.S. degree in electrical engineering from South Dakota State University, Brookings, SD, USA in 2014 and 2016, respectively. Currently, he is working as volunteer research assistant in the Department of Electrical Engineering, South Dakota State University. His research interests include grid integration of renewable energy systems, distributed generation, power electronics, power quality, power systems reliability, computational intelligence, electric vehicles, and internet of energy.(e-mail:dipesh.shrestha@jacks.sdstate.edu)

Zhen Ni (M'15) received the B.S. degree from the Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, China, in 2010, and the Ph.D. degree from the Department of Electrical, Computer and Biomedical Engineering, the University of Rhode Island (URI), Kingston, RI, USA, in 2015. He is currently an Assistant Professor with the Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD, USA. His current research interests include computational intelligence, smart grid, machine learning, and cyber-physical systems. He received the URI Excellence in Doctoral Research Award (2016), Chinese Government Award for Outstanding Students Abroad by Chinese Government (2014), Second Prize of Graduate Student Poster Contest in IEEE Power and Energy Society General Meeting (2015). He has been actively involved in numerous conference and workshop organization committees in the society, including the General Co-Chair of the IEEE CIS Winter School, Washington, DC, USA, in 2016.(e-mail:tonkoski@ieee.org)

Reinaldo Tonkoski (S'04, M'11) received the B.A.Sc. degree in control and automation engineering, in 2004, the M.Sc. degree in electrical engineering in 2006 from PUC-RS (Pontifcia Universidade Catlica do RS), Brazil, and the Ph.D. in 2011 from Concordia University, Canada. He was with CanmetENERGY, Natural Resources Canada, from January 2009 to January 2010 where he worked on projects related to the grid integration of renewable energy sources. Presently, he is an Assistant Professor in the Electrical Engineering and Computer Science Department, South Dakota State University, Brookings, US. Dr. Tonkoski has authored over seventy technical publications in peer reviewed journals and conferences. His research interests include grid integration of renewable energy systems, distributed generation, power quality and power electronics
Corresponding author: Zhen Ni, e-mail:zhen.ni@sdstate.edu
Received Date: 2016-10-26
Accepted Date: 2017-03-30

Abstract

Abstract

Nonlinear loads in the power distribution system cause non-sinusoidal currents and voltages with harmonic components. Shunt active filters (SAF) with current controlled voltage source inverters (CCVSI) are usually used to obtain balanced and sinusoidal source currents by injecting compensation currents. However, CCVSI with traditional controllers have a limited transient and steady state performance. In this paper, we propose an adaptive dynamic programming (ADP) controller with online learning capability to improve transient response and harmonics. The proposed controller works alongside existing proportional integral (PI) controllers to efficiently track the reference currents in the d -q domain. It can generate adaptive control actions to compensate the PI controller. The proposed system was simulated under different nonlinear (three-phase full wave rectifier) load conditions. The performance of the proposed approach was compared with the traditional approach. We have also included the simulation results without connecting the traditional PI control based power inverter for reference comparison. The online learning based ADP controller not only reduced average total harmonic distortion by 18.41 %, but also outperformed traditional PI controllers during transients.

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References(56)

References

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Online Learning Control for Harmonics Reduction Based on Current Controlled Voltage Source Power Inverters

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