NDLI: Detection and classification of micro-grid faults based on HHT and machine learning techniques

Content Provider	IET Digital Library
Author	Mishra, Manohar Rout, Pravat Kumar
Abstract	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.
Starting Page	388
Ending Page	397
Page Count	10
ISSN	17518687
Volume Number	12
e-ISSN	17518695
Issue Number	Issue 2, Jan (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/12/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2017.0502
Journal	IET Generation, Transmission & Distribution
Publisher Date	2017-09-12
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Asymmetrical Fault Bayes Method Decomposition Distributed Power Generation Empirical Mode Decomposition Method Extreme Machine Learning Technique Fault Diagnosis Feature Extraction Grid Connection HHT High Impedance Fault Hilbert Transform Hilbert-Huang Transform IEC Microgrid Model IMF Input Vector Integral Transforms Intrinsic Mode Function Islanding Knowledge Engineering Technique Learning in AI Microgrid Fault Detection Microgrid Fault Event Classification Microgrid Protection Scheme Naive Bayes Classifier Power Engineering Computing Power Generation Fault Power Generation Protection Power Generation Reliability Power System Protection Reliability Statistics Support Vector Machine Symmetrical Fault Three-phase Current Signal Extraction
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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