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Future Internet
Volume 12
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10.3390/fi12020026
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Article

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

by
Jiong Wang
1,2,3,
Hua Zhang
1,2,3,
Dongliang Lin
1,2,3,
Huibin Feng
1,2,3,
Tao Wang
1,2,3,*,
Hongyan Zhang
4,* and
Xiaoding Wang
5
1
College of Computer and Control Engineering, Minjiang University, Fujian 350108, China
2
Industrial Robot Application of Fujian University Engineering Research Center, Minjiang University, Fujian 350108, China
3
Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fujian 350108, China
4
Concord University College Fujian Normal University, Fujian 350117, China
5
College of Mathematics and Informatics, Fujian Provincial Key Laboratory of Network Security and Cryptology, Fujian Normal University, Fujian 350117, China
*
Authors to whom correspondence should be addressed.
Future Internet 2020, 12(2), 26; https://doi.org/10.3390/fi12020026
Submission received: 20 December 2019 / Revised: 17 January 2020 / Accepted: 20 January 2020 / Published: 6 February 2020
(This article belongs to the Special Issue Energy Internet for Green Cities)
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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 ) .
Keywords: fault diagnosis; low-complexity; energy internet; smart city fault diagnosis; low-complexity; energy internet; smart city

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MDPI and ACS Style

Wang, J.; Zhang, H.; Lin, D.; Feng, H.; Wang, T.; Zhang, H.; Wang, X. A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities. Future Internet 2020, 12, 26. https://doi.org/10.3390/fi12020026

AMA Style

Wang J, Zhang H, Lin D, Feng H, Wang T, Zhang H, Wang X. A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities. Future Internet. 2020; 12(2):26. https://doi.org/10.3390/fi12020026

Chicago/Turabian Style

Wang, Jiong, Hua Zhang, Dongliang Lin, Huibin Feng, Tao Wang, Hongyan Zhang, and Xiaoding Wang. 2020. "A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities" Future Internet 12, no. 2: 26. https://doi.org/10.3390/fi12020026

APA Style

Wang, J., Zhang, H., Lin, D., Feng, H., Wang, T., Zhang, H., & Wang, X. (2020). A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities. Future Internet, 12(2), 26. https://doi.org/10.3390/fi12020026

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