MERL - Mitsubishi Electric Research Laboratories

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.

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  • Awards

    •  AWARD    MERL receives 33rd ARIB Radio Achievement Award
      Date: June 28, 2022
      Awarded to: Yukimasa Nagai, Jianlin Guo, Shoichi Kitazawa, Kazuto Yano.
      MERL Contacts: Jianlin Guo; Philip V. Orlik
      Research Areas: Communications, Electric Systems
      Brief
      • Mitsubishi Electric Corporation (Yukimasa Nagai), MERL (Jianlin Guo), Muroran Institute of Technology (Shoichi Kitazawa) and Advanced Telecommunications Research Institute International (Kazuto Yano) jointly won the 33rd ARIB Radio Achievement Award with "IEEE 802.19.3 Standardization and Development for Sub-1 GHz Wireless Frequency Coexistence". The ARIB is an organization similar to the FCC in the U.S. It is responsible for setting standards for all radio communications in Japan at the request of the Ministry of Internal Affairs and Communications (MIC). In order to promote the effective use of radio waves, the "Radio Achievement Award" is given annually to an individual or organization that has made a special achievement in the effective use of radio waves. This award is the most prestigious award in the field of wireless communications in Japan.
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    •  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


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  • Internships

    • MD1917: Learning and Optimization for Motor Drives

      The Electric Machines and Devices team at MERL is seeking motivated and qualified individuals to assist in the development of advanced motor drives technologies. The project goals are twofold: (i) explore model-based optimization methods to design switching voltage sequence for switching losses minimization; (ii) machine learning methods to model the nonlinear flux to current relationship (flux-map) of synchronous machines with spatial harmonics from the real-time experimental data during the commissioning process. The background of the ideal candidate is expected to overlap with at least one of the project goals, if not two. Senior PhD candidates working in mixed integer optimization, model predictive control or machine learning are encouraged to apply. Capability of implementing the algorithm in MATLAB and coding in C are a necessity. Knowledge of motor drives and hands-on experience in real-time systems are a plus. The expected duration of the internship is 3-6 months, preferably onsite at MERL and the start date is flexible.

    • MD1887: Optimization and control of xEV and electric aircraft

      MERL is seeking a motivated and qualified individual to conduct research in modeling, control, simulation and analysis of electric system involved in xEV and electric aircraft. The ideal candidate should have solid backgrounds in some of the following areas: modeling, control, and simulation of electrical systems (including generators, motors, power electronics and batteries), aerodynamics, mission analysis, flight dynamics, and multi-disciplinary system design optimization. Demonstrated experience in software/language such as Modelica or Matlab/Simulink/Simscape is a necessity. Knowledge and experience of CarSim, NPSS, SUAVE, and FLOPS is a definite plus. Senior Ph.D. students in automotive, aerospace, and electrical engineering are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.

    • ST1863: Radar Perception Testbed Engineering (Undergraduate/Master Students)

      MERL is seeking an undergraduate or master student engineering intern to use MERL''s millimeter radar hardware testbed for experiments and data collection, and to maintain the data preprocessing pipeline from raw data acquisition, data organization, annotation, sanitization, and document preparation. Scripting in Python/MATLAB is required. Previous experience with radio frequency (RF) testbed/evaluation kits is preferred. The duration is from September to December with a flexible start date and a part-time option.


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  • Recent Publications

    •  Wang, B., Albader, M., Inoue, H., Kanemaru, M., "Induction Motor Eccentricity Fault Analysis and Quantification with Modified Winding Function based Model", International Conference on Electric Machines and Systems, December 2022.
      BibTeX TR2022-153 PDF
      • @inproceedings{Wang2022dec,
      • author = {Wang, Bingnan and Albader, Mesaad and Inoue, Hiroshi and Kanemaru, Makoto},
      • title = {Induction Motor Eccentricity Fault Analysis and Quantification with Modified Winding Function based Model},
      • booktitle = {International Conference on Electric Machines and Systems},
      • year = 2022,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2022-153}
      • }
    •  Talukder, K., Wang, B., Sakamoto, Y., "Electric Machine Two-dimensional Flux Map Prediction with Ensemble Learning", International Conference on Electrical Machines and Systems, December 2022.
      BibTeX TR2022-152 PDF
      • @inproceedings{Talukder2022dec,
      • author = {Talukder, Khaled and Wang, Bingnan and Sakamoto, Yusuke},
      • title = {Electric Machine Two-dimensional Flux Map Prediction with Ensemble Learning},
      • booktitle = {International Conference on Electrical Machines and Systems},
      • year = 2022,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2022-152}
      • }
    •  Sun, H., Kitamura, S., Nikovski, D.N., "Fair Blackout Rotation for Distribution Systems under Extreme Weather Events", IEEE PES ISGT EUROPE, October 2022.
      BibTeX TR2022-132 PDF
      • @inproceedings{Sun2022oct,
      • author = {Sun, Hongbo and Kitamura, Shoichi and Nikovski, Daniel N.},
      • title = {Fair Blackout Rotation for Distribution Systems under Extreme Weather Events},
      • booktitle = {IEEE PES ISGT EUROPE},
      • year = 2022,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2022-132}
      • }
    •  Wang, B., Lin, C., Inoue, H., Kanemaru, M., "Topological Data Analysis for Electric Motor Eccentricity Fault Detection", Annual Conference of the IEEE Industrial Electronics Society (IECON), October 2022.
      BibTeX TR2022-130 PDF
      • @inproceedings{Wang2022oct2,
      • author = {Wang, Bingnan and Lin, Chungwei and Inoue, Hiroshi and Kanemaru, Makoto},
      • title = {Topological Data Analysis for Electric Motor Eccentricity Fault Detection},
      • booktitle = {Annual Conference of the IEEE Industrial Electronics Society (IECON)},
      • year = 2022,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2022-130}
      • }
    •  Nishanth, F., Wang, B., "Topology Optimization of Electric Machines: A Review", IEEE Energy Conversion Congress and Exposition (ECCE), October 2022.
      BibTeX TR2022-128 PDF
      • @inproceedings{Nishanth2022oct,
      • author = {Nishanth, FNU and Wang, Bingnan},
      • title = {Topology Optimization of Electric Machines: A Review},
      • booktitle = {IEEE Energy Conversion Congress and Exposition (ECCE)},
      • year = 2022,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2022-128}
      • }
    •  Zheng, X., Liu, D., Inoue, H., Kanemaru, M., "Eccentricity Severity Estimation of Induction Machines using a Sparsity-Driven Regression Model", The Fourteenth Annual Energy Conversion Congress and Exposition, October 2022.
      BibTeX TR2022-126 PDF
      • @inproceedings{Zheng2022oct,
      • author = {Zheng, Xiangtian and Liu, Dehong and Inoue, Hiroshi and Kanemaru, Makoto},
      • title = {Eccentricity Severity Estimation of Induction Machines using a Sparsity-Driven Regression Model},
      • booktitle = {The Fourteenth Annual Energy Conversion Congress and Exposition},
      • year = 2022,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2022-126}
      • }
    •  Wang, B., Talukder, K., Sakamoto, Y., "Topological Data Analysis for Image-based Machine Learning: Application to Electric Motors", IEEE International Conference on Electrical Machines (ICEM), DOI: 10.1109/​ICEM51905.2022.9910734, September 2022, pp. 1015-1021.
      BibTeX TR2022-113 PDF
      • @inproceedings{Wang2022sep,
      • author = {Wang, Bingnan and Talukder, Khaled and Sakamoto, Yusuke},
      • title = {Topological Data Analysis for Image-based Machine Learning: Application to Electric Motors},
      • booktitle = {2022 International Conference on Electrical Machines (ICEM)},
      • year = 2022,
      • pages = {1015--1021},
      • month = sep,
      • doi = {10.1109/ICEM51905.2022.9910734},
      • url = {https://www.merl.com/publications/TR2022-113}
      • }
    •  Shirsat, A., Sun, H., Kim, K.J., Guo, J., Nikovski, D.N., "ConvEDNet: A Convolutional Energy Disaggregation Network Using Continuous Point-On-Wave Measurements", IEEE PES General Meeting, DOI: 10.1109/​PESGM48719.2022.9916802, July 2022.
      BibTeX TR2022-101 PDF
      • @inproceedings{Shirsat2022jul,
      • author = {Shirsat, Ashwin and Sun, Hongbo and Kim, Kyeong Jin and Guo, Jianlin and Nikovski, Daniel N.},
      • title = {ConvEDNet: A Convolutional Energy Disaggregation Network Using Continuous Point-On-Wave Measurements},
      • booktitle = {2022 IEEE Power & Energy Society General Meeting (PESGM)},
      • year = 2022,
      • month = jul,
      • doi = {10.1109/PESGM48719.2022.9916802},
      • url = {https://www.merl.com/publications/TR2022-101}
      • }
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