Discrete-Wavelet-Transform and Stockwell-Transform-Based Statistical Parameters Estimation for Fault Analysis in Grid-Connected Wind Power System

2020 ◽  
Vol 14 (3) ◽  
pp. 4320-4328
Author(s):  
Niladri Mukherjee ◽  
Aveek Chattopadhyaya ◽  
Surajit Chattopadhyay ◽  
Samarjit Sengupta
Keyword(s):  
Wavelet Transform ◽  
Power System ◽  
Discrete Wavelet Transform ◽  
Wind Power ◽  
Fault Analysis ◽  
Parameters Estimation ◽  
Discrete Wavelet ◽  
Statistical Parameters ◽  
Wind Power System ◽  
Stockwell Transform

scholarly journals Line to Ground and Line to Line Fault Analysis in IEEE Standard 9 Bus System

2020 ◽  
Vol 93 (1-4) ◽  
pp. 10-18
Author(s):  
Sagnik Datta ◽  
Aveek Chattopadhyaya ◽  
Surajit Chattopadhyay ◽  
Arabinda Das
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Fault Analysis ◽  
Discrete Wavelet ◽  
Statistical Parameters ◽  
Fault Type ◽  
Protection Systems ◽  
Rule Set ◽  
Ieee Standard ◽  
Bus System

Line to Ground (LG) and Line to Line (LL) faults are the two most frequently encountered faults in any power system network. For the purpose of designing advanced protection systems, detection of the location as well as the identification of the type of fault, from a remote location is of paramount importance. In this paper a Discrete Wavelet Transform based statistical analysis has been carried out to detect the fault type and location of LG and LL faults. IEEE standard 9 bus system has been considered for this purpose. Faults are made to occur in the load buses and outgoing currents from the generator buses are analyzed by Discrete Wavelet Transform (DWT) as these current waveforms are non-stationary in nature. Statistical parameters are calculated from the approximate and detail coefficients which have been derived from the DWT. Based upon these parameters, a rule set has also been made. Simulation work is performed with the help of MATLAB. Methods proposed here can be helpful for designing better protection schemes.

scholarly journals Power Quality Disturbances Classification using Discrete Wavelet Transform and Support Vector Machine

2018 ◽  
Vol 7 (4.35) ◽  
pp. 939
Author(s):  
Tiagrajah V. Janahiraman ◽  
Muhammad Hazwan Harun
Keyword(s):  
Machine Learning ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Power Quality ◽  
Service Providers ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Statistical Parameters ◽  
Power Utility ◽  
Power Quality Disturbances

Power utility providers and power industry service providers face a significant challenge in identifying the type of Power Quality Disturbances (PQD) automatically. This paper discusses a method to classify PQD using signal decomposition, statistical analysis and machine learning. Firstly, Discrete Wavelet Transform (DWT) is applied on the generated PQD signals to decompose the signal to obtain its representation in time and frequency domain. Secondly, first and second order statistical parameters are computed on the selected sub-band of DWT. These parameters are used as features vector for the machine learning based classifier. Our database consists of 2400 generated signals of PQD, which were divided into train and test set. Another set of noise corrupted signal database was generated to evaluate the capability of the system. SVM using quadratic kernel was selected as the classifier of the Power Quality Disturbances feature vector. Comparisons were also made with other types of classifiers and other types of mother wavelet filter functions. The results show that the combination of DWT and SVM managed to classify Power Quality Disturbances with high accuracy and has a strong resistance towards noise.  

A novel discrete wavelet transform‐based graphical language classifier for identification of high‐impedance fault in distribution power system

2020 ◽  
Vol 30 (6) ◽  
Author(s):  
Veerapandiyan Veerasamy ◽  
Noor Izzri Abdul Wahab ◽  
Arangarajan Vinayagam ◽  
Mohammad Lutfi Othman ◽  
Rajeswari Ramachandran ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Power System ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Graphical Language ◽  
High Impedance ◽  
High Impedance Fault

scholarly journals WAVELET-BASED TRANSMISSION LINE FAULT DETECTION AND CLASSIFICATION

2013 ◽  
pp. 58-63
Author(s):  
KULKARNISAKEKAR SUMANT SUDHIR ◽  
R.P. HASABE
Keyword(s):  
Wavelet Transform ◽  
Fault Detection ◽  
Power System ◽  
Transmission Line ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Fault Detection And Classification ◽  
Power System Faults ◽  
Detail Coefficient

An appropriate method of fault detection and classification of power system transmission line using discrete wavelet transform is proposed in this paper. The detection is carried out by the analysis of the detail coefficients energy of phase currents. Discrete Wavelet Transform (DWT) analysis of the transient disturbance caused as a result of occurrence faults is performed. The work represented in this paper is focused on classification of simple power system faults using the maximum detail coefficient, energy of the signal and the ratio of energy change of each type of simple simulated fault are characteristic in nature and used for distinguishing fault types.

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