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

Jiong Wang, Hua Zhang, Dongliang Lin, Huibin Feng, Tao Wang, Hongyan Zhang and Xiaoding Wang
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Jiong Wang: College of Computer and Control Engineering, Minjiang University, Fujian 350108, China
Hua Zhang: College of Computer and Control Engineering, Minjiang University, Fujian 350108, China
Dongliang Lin: College of Computer and Control Engineering, Minjiang University, Fujian 350108, China
Huibin Feng: College of Computer and Control Engineering, Minjiang University, Fujian 350108, China
Tao Wang: College of Computer and Control Engineering, 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

Future Internet, 2020, vol. 12, issue 2, 1-12

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 (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2020
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