High Impedance Fault Detection in Medium Voltage Distribution Network Using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System

This paper presents a method to detect and classify the high impedance fault that occur in the medium voltage (MV) distribution network using discrete wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS). The network is designed using MATLAB software R2014b and various faults such as high impedance, symmetrical and unsymmetrical fault have been applied to study the effectiveness of the proposed ANFIS classifier method. This is achieved by training the ANFIS classifier using the features (standard deviation values) extracted from the three-phase fault current signal by DWT technique for various cases of fault with different values of fault resistance in the system. The success and discrimination rate obtained for identifying and classifying the high impedance fault from the proffered method is 100% whereas the values are 66.7% and 85% respectively for conventional fuzzy based approach. The results indicate that the proposed method is more efficient to identify and discriminate the high impedance fault from other faults in the power system.

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High Impedance Fault Detection in MV Distribution Network using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System

10.20944/preprints201810.0687.v1 ◽

2018 ◽

Author(s):

Veerapandiyan Veerasamy ◽

Noor Izzri Abdul Wahab ◽

Rajeswari Ramachandran ◽

Muhammad Mansoor ◽

Mariammal Thirumeni

Keyword(s):

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Discrete Wavelet ◽

Inference System ◽

High Impedance ◽

Neuro Fuzzy ◽

High Impedance Fault ◽

Anfis Classifier

This paper presents a method to detect and classify the high impedance fault that occur in the medium voltage distribution network using discrete wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS). The network is designed using Matlab software and various faults such as high impedance, symmetrical and unsymmetrical fault have been applied to study the effectiveness of the proposed ANFIS classifier method. This is achieved by training the ANFIS classifier using the features (standard deviation values) extracted from the three phase fault current signal by DWT technique for various cases of fault with different values of fault resistance in the system. The success and discrimination rate obtained for identifying and classifying the high impedance fault from the proffered method is 100% whereas the values are 66.7% and 85% respectively for conventional fuzzy based approach. The results indicate that the proposed method is more efficient to identify and discriminate the high impedance fault accurately from other power system faults in the system.

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Hybrid of discrete wavelet transform and adaptive neuro fuzzy inference system for overall driving behavior recognition

Transportation Research Part F Traffic Psychology and Behaviour ◽

10.1016/j.trf.2018.06.044 ◽

2018 ◽

Vol 58 ◽

pp. 782-796 ◽

Cited By ~ 11

Author(s):

Hamid Reza Eftekhari ◽

Mehdi Ghatee

Keyword(s):

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Driving Behavior ◽

Discrete Wavelet ◽

Behavior Recognition ◽

Inference System ◽

Neuro Fuzzy

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Fault location for a series compensated transmission line based on wavelet transform and an adaptive neuro-fuzzy inference system

Proceedings of the 2010 Electric Power Quality and Supply Reliability Conference ◽

10.1109/pq.2010.5549994 ◽

2010 ◽

Cited By ~ 8

Author(s):

El Sayed Mohamed Tag Eldin

Keyword(s):

Wavelet Transform ◽

Transmission Line ◽

Fuzzy Inference System ◽

Fault Location ◽

Fuzzy Inference ◽

Inference System ◽

Neuro Fuzzy

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Fault Localization and Detection in Medium Voltage Distribution Network Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Lecture Notes in Electrical Engineering - Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019 ◽

10.1007/978-981-15-5281-6_74 ◽

2020 ◽

pp. 1039-1052

Author(s):

N. S. B. Jamili ◽

Mohd Rafi Adzman ◽

Wan Syaza Ainaa Wan Salman ◽

M. H. Idris ◽

M. Amirruddin

Keyword(s):

Fuzzy Inference System ◽

Fuzzy Inference ◽

Distribution Network ◽

Fault Localization ◽

Voltage Distribution ◽

Inference System ◽

Medium Voltage ◽

Neuro Fuzzy

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Classification of EEG Signals for Motor Imagery Based on Mutual Information and Adaptive Neuro Fuzzy Inference System

Fuzzy Systems ◽

10.4018/978-1-5225-1908-9.ch016 ◽

2017 ◽

pp. 347-366

Author(s):

Shereen A. El-aal ◽

Rabie A. Ramadan ◽

Neveen Ghali

Keyword(s):

Mutual Information ◽

Motor Imagery ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Imagery Task ◽

Eeg Signals ◽

Inference System ◽

Neuro Fuzzy ◽

Anfis Classifier ◽

Maximal Relevance

Electroencephalogram (EEG) signals based Brain Computer Interface (BCI) is employed to help disabled people to interact better with the environment. EEG signals are recorded through BCI system to translate it to control commands. There are a large body of literature targeting EEG feature extraction and classification for Motor Imagery tasks. Motor imagery task have several features can be extracted to use in classification. However, using more features consume running time and using irrelevant and redundant features affect the performance of the used classifier. This paper is dedicated to extracting the best feature vector for motor imagery task. This work suggests two feature selection methods based on Mutual Information (MI) including Minimum Redundancy Maximal Relevance (MRMR) and maximal Relevance (MaxRel). Adaptive Neuro Fuzzy Inference System (ANFIS) classifier with Subtractive clustering method is utilized for EEG signals classifications. The suggested methods are applied to BCI Competition III dataset IVa and IVb and BCI Competition II dataset III.

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