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 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

    • DA1536: Micro-grid fault analysis and detection

      MERL is seeking a highly motivated and qualified individual to join our summer internship program and conduct research in the area of micro-grid fault analysis and detection. The ideal candidate should have a solid background in micro-grid operation, transient analysis, fault detection, and power system protection. Experience with MATLAB or C/C++/Python is required. The duration of the internship is expected to be 3-6 months, and the start date is flexible. Candidates in their senior or junior years of a Ph.D. program are encouraged to apply. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

    • MD1559: blind signal decomposition

      MERL is seeking a self-motivated intern to work on blind signal decomposition. The ideal candidate would be a senior PhD student with solid background in signal processing, sparse representation, and optimization. Prior experience in array signal processing, compressive sensing, and spectrum analysis is preferred. Skills in Python and/or Matlab are required. The intern is expected to collaborate with MERL researchers to build models, develop algorithms, and prepare manuscripts for scientific publications. The expected duration of the internship is 3 months and the start date is flexible. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

    • MD1479: Electrical Power System Modeling Simulation

      MERL is seeking a motivated and qualified individual to conduct research in modeling, simulation and control of aircraft electrical power system. The ideal candidate should have solid backgrounds in dynamic modeling and simulation of power electronics and electrical machine, and transient analysis of overall electrical power system. Demonstrated experience in physical modeling and simulation software/language such as Modelica or Simscape is a necessity. Knowledge of aircraft dynamics and aerodynamics is a big plus. Senior Ph.D. students in aerospace, electrical engineering, control are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.


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  • Recent Publications

    •  Xiao, D., Sun, H., Nikovski, D.N., Shoichi, K., Mori, K., Hashimoto, H., "CVaR-constrained Stochastic Bidding Strategy for a Virtual Power Plant with Mobile Energy Storages", IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), November 2020.
      BibTeX TR2020-142 PDF
      • @inproceedings{Xiao2020nov,
      • author = {Xiao, Dongliang and Sun, Hongbo and Nikovski, Daniel N. and Shoichi, Kitamura and Mori, Kazuyuki and Hashimoto, Hiroyuki},
      • title = {CVaR-constrained Stochastic Bidding Strategy for a Virtual Power Plant with Mobile Energy Storages},
      • booktitle = {IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)},
      • year = 2020,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2020-142}
      • }
    •  Zhang, S., Ye, F., Wang, B., Habetler, T.G., "Few-Shot Bearing Anomaly Detection via Model-Agnostic Meta-Learning", International Conference on Electrical Machines and Systems (ICEMS), November 2020.
      BibTeX TR2020-151 PDF
      • @inproceedings{Zhang2020nov2,
      • author = {Zhang, Shen and Ye, Fei and Wang, Bingnan and Habetler, Thomas G},
      • title = {Few-Shot Bearing Anomaly Detection via Model-Agnostic Meta-Learning},
      • booktitle = {International Conference on Electrical Machines and Systems (ICEMS)},
      • year = 2020,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2020-151}
      • }
    •  Sun, H., Shoichi, K., Nikovski, D.N., Mori, K., Hashimoto, H., "Illegitimate Trade Detection for Electricity Energy Markets", International Conference on Smart Grids and Energy Systems (SGES), November 2020.
      BibTeX TR2020-147 PDF
      • @inproceedings{Sun2020nov,
      • author = {Sun, Hongbo and Shoichi, Kitamura and Nikovski, Daniel N. and Mori, Kazuyuki and Hashimoto, Hiroyuki},
      • title = {Illegitimate Trade Detection for Electricity Energy Markets},
      • booktitle = {International Conference on Smart Grids and Energy Systems (SGES)},
      • year = 2020,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2020-147}
      • }
    •  Huang, T., Sun, H., Kim, K.J., Nikovski, D.N., Xie, L., "A Holistic Framework for Parameter Coordination of Interconnected Microgrids against Disasters", IEEE Power & Energy Society General Meeting (PES), June 2020.
      BibTeX TR2020-082 PDF
      • @inproceedings{Huang2020jun,
      • author = {Huang, Tong and Sun, Hongbo and Kim, Kyeong Jin and Nikovski, Daniel N. and Xie, Le},
      • title = {A Holistic Framework for Parameter Coordination of Interconnected Microgrids against Disasters},
      • booktitle = {IEEE Power & Energy Society General Meeting (PES)},
      • year = 2020,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2020-082}
      • }
    •  Tian, N., Fang, H., Chen, J., Wang, Y., "Nonlinear Double-Capacitor Model for Rechargeable Batteries: Modeling, Identification and Validation", IEEE Transactions on Control Systems Technology, DOI: 10.1109/TCST.2020.2976036, pp. 1-15, April 2020.
      BibTeX TR2020-035 PDF
      • @article{Tian2020apr,
      • author = {Tian, Ning and Fang, Huazhen and Chen, Jian and Wang, Yebin},
      • title = {Nonlinear Double-Capacitor Model for Rechargeable Batteries: Modeling, Identification and Validation},
      • journal = {IEEE Transactions on Control Systems Technology},
      • year = 2020,
      • pages = {1--15},
      • month = apr,
      • doi = {10.1109/TCST.2020.2976036},
      • url = {https://www.merl.com/publications/TR2020-035}
      • }
    •  Poudel, S., Sun, H., Nikovski, D.N., Zhang, J., "Distributed Average Consensus Algorithm for Damage Assessment of Power Distribution system", IEEE PES Innovative Smart Grid Technologies Conference (ISGT), DOI: 10.1109/ISGT45199.2020.9087643, February 2020.
      BibTeX TR2020-013 PDF
      • @inproceedings{Poudel2020feb,
      • author = {Poudel, Shiva and Sun, Hongbo and Nikovski, Daniel N. and Zhang, Jinyun},
      • title = {Distributed Average Consensus Algorithm for Damage Assessment of Power Distribution system},
      • booktitle = {IEEE PES Innovative Smart Grid Technologies Conference (ISGT)},
      • year = 2020,
      • month = feb,
      • doi = {10.1109/ISGT45199.2020.9087643},
      • issn = {2472-8152},
      • isbn = {978-1-7281-3103-0},
      • url = {https://www.merl.com/publications/TR2020-013}
      • }
    •  Xu, H., Sun, H., Nikovski, D.N., Kitamura, S., Mori, K., Hashimoto, H., "Deep Reinforcement Learning for Joint Bidding and Pricing of Load Serving Entity", IEEE Transactions on smart grids, DOI: 10.1109/TSG.2019.2903756, Vol. 10, No. 6, pp. 6366-6375, January 2020.
      BibTeX TR2020-003 PDF
      • @article{Xu2020jan,
      • author = {Xu, Hanchen and Sun, Hongbo and Nikovski, Daniel N. and Kitamura, Shoichi and Mori, Kazuyuki and Hashimoto, Hiroyuki},
      • title = {Deep Reinforcement Learning for Joint Bidding and Pricing of Load Serving Entity},
      • journal = {IEEE Transactions on smart grids},
      • year = 2020,
      • volume = 10,
      • number = 6,
      • pages = {6366--6375},
      • month = jan,
      • doi = {10.1109/TSG.2019.2903756},
      • issn = {1949-3061},
      • url = {https://www.merl.com/publications/TR2020-003}
      • }
    •  Zhang, S., Ye, F., Wang, B., Habetler, T.G., "Semi-Supervised Learning of Bearing Anomaly Detection via Deep Variational Autoencoders", arXiv, November 2019.
      BibTeX arXiv
      • @article{Zhang2019nov,
      • author = {Zhang, Shen and Ye, Fei and Wang, Bingnan and Habetler, Thomas G},
      • title = {Semi-Supervised Learning of Bearing Anomaly Detection via Deep Variational Autoencoders},
      • journal = {arXiv},
      • year = 2019,
      • month = nov,
      • url = {https://arxiv.org/abs/1912.01096}
      • }
    See All Publications for Electric Systems