Electric Systems

Modeling & optimization of power systems and electromagnetic machines.

Our research in this area includes flexible and resilient power system design and operational optimization; modeling and analysis of electric machines for applications such as fault detection of motors, power efficiency improvement and design complexity reduction.

  • Researchers

  • Awards

    •  AWARD    Best conference paper of IEEE PES-GM 2020
      Date: June 18, 2020
      Awarded to: Tong Huang, Hongbo Sun, K.J. Kim, Daniel Nikovski, Le Xie
      MERL Contacts: Kyeong Jin (K.J.) Kim; Daniel N. Nikovski; Hongbo Sun
      Research Areas: Data Analytics, Electric Systems, Optimization
      Brief
      • A paper on A Holistic Framework for Parameter Coordination of Interconnected Microgrids Against Natural Disasters, written by Tong Huang, a former MERL intern from Texas A&M University, has been selected as one of the Best Conference Papers at the 2020 Power and Energy Society General Meeting (PES-GM). IEEE PES-GM is the flagship conference for the IEEE Power and Energy Society. The work was done in collaboration with Hongbo Sun, K. J. Kim, and Daniel Nikovski from MERL, and Tong's advisor, Prof. Le Xie from Texas A&M University.
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  • News & Events

    •  TALK    [MERL Seminar Series 2021] Use the [Magnetic] Force for Good: Sustainability Through Magnetic Levitation
      Date & Time: Tuesday, December 7, 2021; 1:00 PM EST
      Speaker: Prof. Eric Severson, University of Wisconsin-Madison
      MERL Host: Bingnan Wang
      Research Area: Electric Systems
      Abstract
      • Electric motors pump our water, heat and cool our homes and offices, drive critical medical and surgical equipment, and, increasingly, operate our transportation systems. Approximately 99% of the world’s electric energy is produced by a rotating generator and 45% of that energy is consumed by an electric motor. The efficiency of this technology is vital in enabling our energy sustainability and reducing our carbon footprint. The reliability and lifetime of this technology have severe, and sometimes life-altering, consequences. Today’s motor technology largely relies upon mechanical bearings to support the motor’s shaft. These bearings are the first components to fail, create frictional losses, and rely on lubricants that create contamination challenges and require periodic maintenance. In short, bearings are the Achilles' heel of modern electric motors.

        This seminar will explore the use of actively controlled magnetic forces to levitate the motor shaft, eliminating mechanical bearings and the problems associated with them. The working principles of traditional magnetic levitation technology (active magnetic bearings) will be reviewed and used to explain why this technology has not been successfully applied to the most high-impact motor applications. Research into “bearingless” motors offers a new levitation approach by manipulating the inherent magnetic force capability of all electric motors. While traditional motors are carefully designed to prevent shaft forces, the bearingless motor concept controls these forces to make the motor simultaneously function as an active magnetic bearing. The seminar will showcase the potential of bearingless technology to revolutionize motor systems of critical importance for energy and sustainability—from industrial compressors and blowers, such as those found in HVAC systems and wastewater aeration equipment, to power grid flywheel energy storage devices and electric turbochargers in fuel-efficient vehicles.
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    •  EVENT    Prof. Melanie Zeilinger of ETH to give keynote at MERL's Virtual Open House
      Date & Time: Thursday, December 9, 2021; 1:00pm - 5:30pm EST
      Location: Virtual Event
      Speaker: Prof. Melanie Zeilinger, ETH
      Research Areas: Applied Physics, Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Electric Systems, Electronic and Photonic Devices, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Digital Video, Human-Computer Interaction, Information Security
      Brief
      • MERL is excited to announce the second keynote speaker for our Virtual Open House 2021:
        Prof. Melanie Zeilinger from ETH .

        Our virtual open house will take place on December 9, 2021, 1:00pm - 5:30pm (EST).

        Join us to learn more about who we are, what we do, and discuss our internship and employment opportunities. Prof. Zeilinger's talk is scheduled for 3:15pm - 3:45pm (EST).

        Registration: https://mailchi.mp/merl/merlvoh2021

        Keynote Title: Control Meets Learning - On Performance, Safety and User Interaction

        Abstract: With increasing sensing and communication capabilities, physical systems today are becoming one of the largest generators of data, making learning a central component of autonomous control systems. While this paradigm shift offers tremendous opportunities to address new levels of system complexity, variability and user interaction, it also raises fundamental questions of learning in a closed-loop dynamical control system. In this talk, I will present some of our recent results showing how even safety-critical systems can leverage the potential of data. I will first briefly present concepts for using learning for automatic controller design and for a new safety framework that can equip any learning-based controller with safety guarantees. The second part will then discuss how expert and user information can be utilized to optimize system performance, where I will particularly highlight an approach developed together with MERL for personalizing the motion planning in autonomous driving to the individual driving style of a passenger.
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  • Internships

    • CI1752: Machine Learning for Electric Design Automation

      MERL is seeking a highly motivated and qualified intern to join the Signal Processing group for an internship program. The ideal candidate will be expected to carry out research on machine learning for automated design synthesis to improve hardware efficiency of various digital signal processing algorithms. The candidate is expected to have solid knowledge of deep learning, reinforcement learning, symbolic learning, decision making, and graph neural networks. Hands-on experience of high-level synthesis, FPGA prototyping, verilog, and general digital signal processing is a plus.

    • MD1714: Electric Motor Design

      MERL is seeing a motivated and qualified individual to conduct research on electric machine design, prototype, and experiment tests. The ideal candidate should have solid background and demonstrated research experience in electric machine theory, design analysis, motor drives, and control. Hands-on experiences on electric motor design and prototyping, test bench set up, and experiment measurements are required. Senior Ph.D. students in electrical engineering or mechanical engineering with related expertise are encouraged to apply. Start date for this internship is flexible.

    • MD1746: PWM inverter circuit design

      MERL is looking for a self-motivated intern to work on PWM inverter drive circuit design and fabrication. The ideal candidate would be a Ph.D. candidate in electrical engineering with solid research background in power electronics. Experience in PWM inverter design, switching loss estimation, and EMI is desired. The intern is expected to collaborate with MERL researchers to design, simulate, and fabricate circuits, carry out experiments, analyze experimental data, and prepare manuscripts for scientific publications. The total duration is 3 months.


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  • Recent Publications

    •  Wang, B., Zhou, L., Miyoshi, M., Inoue, H., Kanemaru, M., "Quantification of Induction Motor Bearing Fault Severity based on Modified Winding Function Theory", International Conference on Electrical Machines and Systems (ICEMS), DOI: 10.23919/​ICEMS52562.2021.9634328, November 2021, pp. 944-948.
      BibTeX TR2021-139 PDF
      • @inproceedings{Wang2021nov3,
      • author = {Wang, Bingnan and Zhou, Lei and Miyoshi, Masahito and Inoue, hiroshi and Kanemaru, Makoto},
      • title = {Quantification of Induction Motor Bearing Fault Severity based on Modified Winding Function Theory},
      • booktitle = {2021 24th International Conference on Electrical Machines and Systems (ICEMS)},
      • year = 2021,
      • pages = {944--948},
      • month = nov,
      • publisher = {IEEE},
      • doi = {10.23919/ICEMS52562.2021.9634328},
      • url = {https://www.merl.com/publications/TR2021-139}
      • }
    •  Zhang, Z., Liu, D., "A Graph-based Method to Extract Broken Rotor Bar Fault Signature in Varying Speed Operation", ICEMS 2021, DOI: 10.23919/​ICEMS52562.2021.9634664, October 2021.
      BibTeX TR2021-135 PDF
      • @inproceedings{Zhang2021oct,
      • author = {Zhang, Zhe and Liu, Dehong},
      • title = {A Graph-based Method to Extract Broken Rotor Bar Fault Signature in Varying Speed Operation},
      • booktitle = {ICEMS 2021},
      • year = 2021,
      • month = oct,
      • publisher = {IEEE},
      • doi = {10.23919/ICEMS52562.2021.9634664},
      • issn = {2642-5513},
      • isbn = {978-8-9865-1021-8},
      • url = {https://www.merl.com/publications/TR2021-135}
      • }
    •  Sun, H., Kawano, S., Nikovski, D.N., Takano, T., Mori, K., "Distribution system fault location analysis using graph neural network with node and link attributes", IEEE PES Innovative Smart Grid Technologies Conference - Europe (ISGT Europe), DOI: 10.1109/​ISGTEurope52324.2021.9639928, October 2021.
      BibTeX TR2021-130 PDF
      • @inproceedings{Sun2021oct,
      • author = {Sun, Hongbo and Kawano, Shunsuke and Nikovski, Daniel N. and Takano, Tomihiro and Mori, Kazuyuki},
      • title = {Distribution system fault location analysis using graph neural network with node and link attributes},
      • booktitle = {IEEE PES Innovative Smart Grid Technologies Conference - Europe (ISGT Europe)},
      • year = 2021,
      • month = oct,
      • doi = {10.1109/ISGTEurope52324.2021.9639928},
      • url = {https://www.merl.com/publications/TR2021-130}
      • }
    •  Wang, B., Shin, K.-H., Hidaka, Y., Kondo, S., Arita, H., Ito, K., "Analytical Magnetic Model for Variable-Flux Interior Permanent Magnet Synchronous Motors", IEEE Energy Conversion Congress and Exposition (ECCE), DOI: 10.1109/​ECCE47101.2021.9595341, October 2021, pp. 4142-4148.
      BibTeX TR2021-123 PDF
      • @inproceedings{Wang2021oct2,
      • author = {Wang, Bingnan and Shin, Kyung-Hun and Hidaka, Yuki and Kondo, Shota and Arita, Hideaki and Ito, Kazumasa},
      • title = {Analytical Magnetic Model for Variable-Flux Interior Permanent Magnet Synchronous Motors},
      • booktitle = {2021 IEEE Energy Conversion Congress and Exposition (ECCE)},
      • year = 2021,
      • pages = {4142--4148},
      • month = oct,
      • publisher = {IEEE},
      • doi = {10.1109/ECCE47101.2021.9595341},
      • url = {https://www.merl.com/publications/TR2021-123}
      • }
    •  Wang, S., Sun, H., Kim, K.J., Guo, J., Nikovski, D.N., "Solving Bernoulli Bandit Problems for Weather-relative Overhead Distribution Line Failures Forecasting", IEEE PES GM, DOI: 10.1109/​PESGM46819.2021.9638153, July 2021.
      BibTeX TR2021-087 PDF
      • @inproceedings{Wang2021jul2,
      • author = {Wang, Shengyi and Sun, Hongbo and Kim, Kyeong Jin and Guo, Jianlin and Nikovski, Daniel N.},
      • title = {Solving Bernoulli Bandit Problems for Weather-relative Overhead Distribution Line Failures Forecasting},
      • booktitle = {IEEE PES GM},
      • year = 2021,
      • month = jul,
      • doi = {10.1109/PESGM46819.2021.9638153},
      • url = {https://www.merl.com/publications/TR2021-087}
      • }
    •  Zhang, S., Ye, F., Wang, B., Habetler, T.G., "Few-Shot Bearing Fault Diagnosis Based on Model-Agnostic Meta-Learning", IEEE Transactions on Industry Applications, DOI: 10.1109/​TIA.2021.3091958, June 2021.
      BibTeX TR2021-081 PDF
      • @article{Zhang2021jun,
      • author = {Zhang, Shen and Ye, Fei and Wang, Bingnan and Habetler, Thomas G},
      • title = {Few-Shot Bearing Fault Diagnosis Based on Model-Agnostic Meta-Learning},
      • journal = {IEEE Transactions on Industry Applications},
      • year = 2021,
      • month = jun,
      • doi = {10.1109/TIA.2021.3091958},
      • url = {https://www.merl.com/publications/TR2021-081}
      • }
    •  Zhou, L., Guo, F., Wang, H., Wang, B., "High-Torque Direct-Drive Machine with Combined Axial- and Radial-flux Out-runner Vernier Permanent Magnet Motor", International Electric Machine & Drives Conference (IEMDC), DOI: 10.1109/​IEMDC47953.2021.9449499, May 2021, pp. 1-8.
      BibTeX TR2021-050 PDF
      • @inproceedings{Zhou2021may,
      • author = {Zhou, Lei and Guo, Feng and Wang, Hongyu and Wang, Bingnan},
      • title = {High-Torque Direct-Drive Machine with Combined Axial- and Radial-flux Out-runner Vernier Permanent Magnet Motor},
      • booktitle = {2021 IEEE International Electric Machines Drives Conference (IEMDC)},
      • year = 2021,
      • pages = {1--8},
      • month = may,
      • publisher = {IEEE},
      • doi = {10.1109/IEMDC47953.2021.9449499},
      • url = {https://www.merl.com/publications/TR2021-050}
      • }
    •  Chen, D., Danielson, C., Masahiro, I., "Improving Passenger Comfort by Exploiting Hub Motors in Electric Vehicles: Suspension Modeling", ASME Dynamic Systems and Control Conference (DSCC), DOI: 10.1115/​DSCC2020-3167, January 2021.
      BibTeX TR2021-019 PDF
      • @inproceedings{Chen2021jan,
      • author = {Chen, Di and Danielson, Claus and Masahiro, Iezawa},
      • title = {Improving Passenger Comfort by Exploiting Hub Motors in Electric Vehicles: Suspension Modeling},
      • booktitle = {ASME Dynamic Systems and Control Conference (DSCC)},
      • year = 2021,
      • month = jan,
      • doi = {10.1115/DSCC2020-3167},
      • url = {https://www.merl.com/publications/TR2021-019}
      • }
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  • Videos