NDLI: Transmission line fault classification using modified fuzzy Q learning

Content Provider	IET Digital Library
Author	Malik, Hasmat Sharma, Rajneesh
Abstract	The authors propose an adaptive, self-learning fault classifier based on modified fuzzy Q learning (MFQL) for transmission lines. Proposed MFQL fault classifier is able to achieve very high classification accuracy with relatively small number of samples. The authors’ is a first attempt at designing a fault identifier using reinforcement learning for fault segregation in transmission lines. The authors’ identifier does not assume prior knowledge of transmission line model or target fault information. Raw voltage and current data (supply and load side) is processed using empirical mode decomposition to generate 13 intrinsic mode functions (IMFs’). Classifier employs the J48 algorithm to further prune these 13 IMF's to eight most relevant input variables, which serve as inputs to the MFQL fault classifier. The authors compare performance of the proposed MFQL classifier to other contemporary AI-based classifiers, e.g. neural networks and support vector machines. Simulation results and performance comparison against other AI-based classifiers elucidates that the proposed MFQL-based identifier achieves a significantly higher performance level and could serve as an important tool for transmission line fault diagnosis.
Starting Page	4041
Ending Page	4050
Page Count	10
ISSN	17518687
Volume Number	11
e-ISSN	17518695
Issue Number	Issue 16, Nov (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/11/16
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2017.0331
Journal	IET Generation, Transmission & Distribution
Publisher Date	2017-07-11
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Combinatorial Mathematics Contemporary AI-based Classifier Current Data Empirical Mode Decomposition Fault Diagnosis Fault Identifier Fault Segregation Fuzzy Set Theory IMF Intrinsic Mode Function J48 Algorithm Knowledge Engineering Technique Learning in AI MFQL Classifier Modified Fuzzy Q Learning Neural Computing Technique Neural Nets Neural Network Power Engineering Computing Power Transmission Fault Power Transmission Line Power Transmission, Distribution And Supply Raw Voltage Reinforcement Learning Self-learning Fault Classifier Support Vector Machine SVM Target Fault Information Transmission Line Fault Classification
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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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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