access icon free Detection and classification of micro-grid faults based on HHT and machine learning techniques

© The Institution of Engineering and Technology
Received 04/04/2017, Accepted 03/09/2017, Revised 04/08/2017, Published 12/09/2017

This study presents a novel micro-grid protection scheme based on Hilbert–Huang transform (HHT) and machine learning techniques. Initialisation of the proposed approach is done by extracting the three-phase current signals at the targeted buses of different feeders. The obtained non-stationary signals are passed through the empirical mode decomposition method to extract different intrinsic mode functions (IMFs). In the next step using HHT to the selected IMFs component, different needful differential features are computed. The extracted features are further used as an input vector to the machine learning models to classify the fault events. The proposed micro-grid protection scheme is tested for different protection scenarios, such as the type of fault (symmetrical, asymmetrical and high impedance fault), micro-grid structure (radial and mesh) and mode of operation (islanded and grid connected) and so on. Three different machine learning models are tested and compared in this framework: Naive Bayes classifier, support vector machine and extreme learning machine. The extensive simulated results from a standard IEC micro-grid model prove the effectiveness and reliability of the proposed micro-grid protection scheme.

Inspec keywords: Hilbert transforms; power generation faults; fault diagnosis; power generation reliability; power engineering computing; feature extraction; decomposition; learning (artificial intelligence); power generation protection; support vector machines; Bayes methods; distributed power generation

Other keywords: input vector; symmetrical fault; intrinsic mode function; three-phase current signal extraction; islanding; feature extraction; extreme machine learning technique; microgrid fault event classification; empirical mode decomposition method; naive Bayes classifier; Hilbert-Huang transform; IEC microgrid model; grid connection; asymmetrical fault; reliability; microgrid fault detection; IMF; high impedance fault; support vector machine; HHT; microgrid protection scheme

Subjects: Distributed power generation; Reliability; Integral transforms; Other topics in statistics; Power engineering computing; Knowledge engineering techniques; Other topics in statistics; Integral transforms; Power system protection

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