Microgrid Fault Detection and Classification: Machine Learning Based Approach, Comparison, and Reviews

Shahriar Rahman Fahim, Subrata K. Sarker, S. M. Muyeen, Md. Rafiqul Islam Sheikh and Sajal K. Das
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Shahriar Rahman Fahim: Department of Electrical and Electronic Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
Subrata K. Sarker: Department of Electrical and Electronic Engineering, Varendra University, Rajshahi 6204, Bangladesh
S. M. Muyeen: School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth 6845, Australia
Md. Rafiqul Islam Sheikh: Department of Electrical and Electronic Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
Sajal K. Das: Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh

Energies, 2020, vol. 13, issue 13, 1-22

Abstract: Accurate fault classification and detection for the microgrid (MG) becomes a concern among the researchers from the state-of-art of fault diagnosis as it increases the chance to increase the transient response. The MG frequently experiences a number of shunt faults during the distribution of power from the generation end to user premises, which affects the system reliability, damages the load, and increases the fault line restoration cost. Therefore, a noise-immune and precise fault diagnosis model is required to perform the fast recovery of the unhealthy phases. This paper presents a review on the MG fault diagnosis techniques with their limitations and proposes a novel discrete-wavelet transform (DWT) based probabilistic generative model to explore the precise solution for fault diagnosis of MG. The proposed model is made of multiple layers with a restricted Boltzmann machine (RBM), which enables the model to make the probability reconstruction over its inputs. The individual RBM layer is trained with an unsupervised learning approach where an artificial neural network (ANN) algorithm tunes the model for minimizing the error between the true and predicted class. The effectiveness of the proposed model is studied by varying the input signal and sampling frequencies. A level of considered noise is added with the sample data to test the robustness of the studied model. Results prove that the proposed fault detection and classification model has the ability to perform the precise diagnosis of MG faults. A comparative study among the proposed, kernel extreme learning machine (KELM), multi KELM, and support vector machine (SVM) approaches is studied to confirm the robust superior performance of the proposed model.

Keywords: deep belief network; faults; microgrid; distribution line; wavelet transform (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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