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A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities

Author

Listed:
  • Jiong Wang

    (College of Computer and Control Engineering, Minjiang University, Fujian 350108, China
    Industrial Robot Application of Fujian University Engineering Research Center, Minjiang University, Fujian 350108, China
    Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fujian 350108, China)

  • Hua Zhang

    (College of Computer and Control Engineering, Minjiang University, Fujian 350108, China
    Industrial Robot Application of Fujian University Engineering Research Center, Minjiang University, Fujian 350108, China
    Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fujian 350108, China)

  • Dongliang Lin

    (College of Computer and Control Engineering, Minjiang University, Fujian 350108, China
    Industrial Robot Application of Fujian University Engineering Research Center, Minjiang University, Fujian 350108, China
    Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fujian 350108, China)

  • Huibin Feng

    (College of Computer and Control Engineering, Minjiang University, Fujian 350108, China
    Industrial Robot Application of Fujian University Engineering Research Center, Minjiang University, Fujian 350108, China
    Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fujian 350108, China)

  • Tao Wang

    (College of Computer and Control Engineering, Minjiang University, Fujian 350108, China
    Industrial Robot Application of Fujian University Engineering Research Center, Minjiang University, Fujian 350108, China
    Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fujian 350108, China)

  • Hongyan Zhang

    (Concord University College Fujian Normal University, Fujian 350117, China)

  • Xiaoding Wang

    (College of Mathematics and Informatics, Fujian Provincial Key Laboratory of Network Security and Cryptology, Fujian Normal University, Fujian 350117, China)

Abstract

The smart energy system, viewed as an “Energy Internet”, consists of the intelligent integration of decentralized sustainable energy sources, efficient distribution, and optimized power consumption. That implies the fault diagnosis for a smart energy system should be of low complexity. In this paper, we propose a Strong Tracking Unscented Kalman Filter ( S T U K F ) and modified Bayes’ classification-based Modified Three Sigma test ( M T S ), abbreviated as S F B T , for smart energy networks. The theoretical analysis and simulations indicate that S F B T detects faults with a high accuracy and a low complexity of O ( n ) .

Suggested Citation

  • Jiong Wang & Hua Zhang & Dongliang Lin & Huibin Feng & Tao Wang & Hongyan Zhang & Xiaoding Wang, 2020. "A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities," Future Internet, MDPI, vol. 12(2), pages 1-12, February.
    • Handle: RePEc:gam:jftint:v:12:y:2020:i:2:p:26-:d:317164
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    References listed on IDEAS

    as
    1. Zhao, Liqiang & Wang, Jianlin & Yu, Tao & Jian, Huan & Liu, Tangjiang, 2015. "Design of adaptive robust square-root cubature Kalman filter with noise statistic estimator," Applied Mathematics and Computation, Elsevier, vol. 256(C), pages 352-367.
    2. Rafael Cisneros-Magaña & Aurelio Medina & Olimpo Anaya-Lara, 2018. "Time-Domain Voltage Sag State Estimation Based on the Unscented Kalman Filter for Power Systems with Nonlinear Components," Energies, MDPI, vol. 11(6), pages 1-20, June.
    3. Jian Ma & Chen Lu & Hongmei Liu, 2015. "Fault Diagnosis for the Heat Exchanger of the Aircraft Environmental Control System Based on the Strong Tracking Filter," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-11, March.
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