Intelligent Fault Classification and Location Identification Method for Microgrids Using Discrete Orthonormal Stockwell Transform-Based Optimized Multi-Kernel Extreme Learning Machine

Teke Gush, Syed Basit Ali Bukhari, Khawaja Khalid Mehmood, Samuel Admasie, Ji-Soo Kim and Chul-Hwan Kim
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Teke Gush: College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Korea
Syed Basit Ali Bukhari: U.S.-Pakistan Center for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), H-12 Campus, Islamabad 44000, Pakistan
Khawaja Khalid Mehmood: U.S.-Pakistan Center for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), H-12 Campus, Islamabad 44000, Pakistan
Samuel Admasie: College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Korea
Ji-Soo Kim: College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Korea
Chul-Hwan Kim: College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Korea

Energies, 2019, vol. 12, issue 23, 1-16

Abstract: This paper proposes an intelligent fault classification and location identification method for microgrids using discrete orthonormal Stockwell transform (DOST)-based optimized multi-kernel extreme learning machine (MKELM). The proposed method first extracts useful statistical features from one cycle of post-fault current signals retrieved from sending-end relays of microgrids using DOST. Then, the extracted features are normalized and fed to the MKELM as an input. The MKELM, which consists of multiple kernels in the hidden nodes of an extreme learning machine, is used for the classification and location of faults in microgrids. A genetic algorithm is employed to determine the optimum parameters of the MKELM. The performance of the proposed method is tested on the standard IEC microgrid test system for various operating conditions and fault cases, including different fault locations, fault resistance, and fault inception angles using the MATLAB/Simulink software. The test results confirm the efficacy of the proposed method for classifying and locating any type of fault in a microgrid with high performance. Furthermore, the proposed method has higher performance and is more robust to measurement noise than existing intelligent methods.

Keywords: discrete orthonormal Stockwell transform; distributed energy resources; fault classification; fault location; microgrid; multi-kernel extreme learning machine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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