Transmission line fault classification using discrete wavelet transform (original) (raw)

Fault Classification in Transmission Systems using Wavelet Transform

GAZI UNIVERSITY JOURNAL OF SCIENCE

Highlights • Detection and classification of faults are extremely essential in power system. • A novel fault detection technique is proposed for transmission line. • Fault classification methodology is proposed using wavelet transform. • The accuracy in fault classification was improved with proposed methodology.

Transmission Line Fault Detection Using Wavelet Transform

Proper detection of various faults occurring on the transmission line is very essential. In this paper, detection and classification of some these faults is done based on the information conveyed by the wavelet analysis of power systems transients. Maximum norm values, maximum detail coefficient, energy of the current signals are calculated from the Wavelet Toolbox in MATLAB/Simulink. Maximum norm value and energy of the signals detects the fault and threshold detail coefficient classifies the fault into different types such as L-G, L-L-G, L-L-L.

Identification and Classification of Transmission Line Faults Using Wavelet Analysis

An accurate fault detection and classification is required to transmit power from generating station to various load centers reliably. Transmission line protection is mainly based on circuit breaker active tripping's. This tripping action depends on the voltage and current waveforms during the fault. Wavelet analysis which is a signal processing tool to detect and analyze the fault occurring in transmission line. Discrete wavelet transform (DWT) is used for the analysis of the current waveform during the fault. An approach for transmission line classification also described in this paper. MATLAB/Simulink software is used to illustrate the effectiveness of the proposed approach, an extensive simulation studies have been carried out for different types of faults.

Wavelet Transform Technique for Fault Detection on Power System Transmission Line

Iconic Research and Engineering Journals, 2019

This paper presents a discrete wavelet transform and neural network approach to fault detection and classification in transmission line faults. The detection is carried out by the analysis of the detail's coefficients energy of the phase signals, and as an input to neural network to classify the faults on transmission lines. Neural network performs well when faced with different fault conditions and system parameters. In this paper, WT has been applied to the output phase A unbalance fault voltage and current signals of a typical transmission line modeled with MATLAB/SIMULINK 2016. The Phase A were simulated on the line and their pre-fault and fault voltage and current per-unit output values were generated and produced waveforms of pre-fault and fault signals. The results of the MRA fault detection analysis show that the wavelet transform method is more accurate in detecting the various faults of a transmission line than any other signal analysis techniques.

Faults Detection And Classification On Long Transmission Line Using Wavelet Analysis

The disturbances of power systems are aperiodic, non stationary, very short duration and impulsive in nature. The wavelet transform based approaches have been successfully detect and classify the faults due to their ability to express the signal both in frequency and time domain. In this paper, Wavelet Transform based fault detection and classification technique has been proposed. The fault classification technique is developed on the basis of extensive simulation studies carried out on the power system model using SimPowerSystems MATLAB toolbox for different operating conditions. This method detects all ten types of faults (e.g., a-g, b-g, c-g, a-b, b-c, c-a, a-b-g, b-c-g, c-a-g, ab-c) on the transmission line. Fault data generated by workspace on MATLAB simulation model have been used for fault detection and classification by MATLAB wavelet toolbox. The maximum threshold ratio using remove near zero method and percentage of energy level at highest approximation level of each type of fault are characteristics in nature and are used for distinguishing the fault types.

Classification of Transmission Line Faults Using Wavelet Transformer.

International Journal of Engineering Sciences & Research Technology, 2014

In general fault analysis is carried out for a given system assuming various types of fault currents are estimated based on the configurations. It is proposed to implement Discrete Wavelet Transformer (DWT) approach for fault classification once the fault currents are known in a particular location. Daubechies eight (D-8) wavelet transforms of the three phase currents on a transmission line fed from both ends is used. An algorithm is implemented for the classification of faults in the transmission line using MATLAB-SIMULINK software. The advent of large generating stations and highly interconnected power systems makes early fault detection and rapid equipment isolation imperative to maintain system stability. In analyzing the fault are existing methods like Fourier analysis, Short Time Fourier Transforms and Fast Fourier Transforms have the limitations of fixed window and poor resolution. If wavelet transforms are opted, they overcome the above disadvantages, as wavelet transforms employ analysis functions that are localized both in time and frequency domain. It focuses on short-time intervals for high frequency components and long-time intervals for low frequency components. Wavelets have a window that automatically adapts to give appropriate resolution.

WAVELET TRANSFORM BASED FAULT DETECTION AND CLASSIFICATION IN TRANSMISSION LINE

This paper presents a Wavelet Transform based approach to detect and classify different shunt faults that may occur in transmission lines. The algorithm is based mainly on calculating the RMS values of the wavelet coefficients of current signals at both the ends of the transmission line over a moving window length of half cycle.The current signals are analyzed with 'db4' wavelet to obtain detail coefficients and compared with threshold values to detect and classify the faults. To illustrate effectiveness of proposed technique extensive simulation studies using PSCAD/EMTDC and MATLAB have been carried out for different types of faults considering wide variations in fault resistances, inception angle and loading levels. Fault data generated by PSCAD/EMTDC have been used for fault detection and classification by a MATLAB program. Thus, the proposed technique is well suited for implementation in digital distance protection schemes. and 0.8L, where L is the length of the line. All these locations have proved the accuracy of the proposed technique.

A NOVEL METHOD FOR DETECTION OF ELECTRIC TRANSMISSION LINE FAULTS USING DISCRETE WAVELET TRANSFORM

The power utility companies have been trying to identify and locate three-phase transmission line faults in the minimum possible time in order to prevent economic losses. In the last few decades technology used for power system protection has evolved and shifted from electromechanical devices to solid state and micro-processor based intelligent devices, which require fast and accurate detection of faults in the transmission lines. This paper presents a novel discrete wavelet transform (DWT) based mult i-resolution analysis technique for detection of transmission line faults including and without including ground. A comparative study of all types of faults is presented. A test system having generation, load and transmission line in two parts is modeled in MATLAB/ Simulink environment. The MATLAB programming is used for DWT analysis of the faults.

Wavelet Feature Based Fault Detection and Classification Technique for Transmission line Protection

In the present scenario, the efficiency of a power system depends on how a fault is accurately detected and classified, so that quick restoration and maintenance of power is accomplished. Fault detection, fault classification, needs to be performed using a fast and responsive algorithm at different levels of a power system. Effect of factors such as fault impedance, fault inception angle (FIA), and fault distance, which cause disturbances in power line can be analyzed by Wavelet based multi resolution analysis (MRA). This paper proposed, a fault detection and classification technique using MRA based on wavelet transform. The present paper also deals with the exploration of advantages and problems related with the proposed fault detection and classification technique. The method of fault detection and classification proposed in this work is based on the three-phase current and voltage waveforms measured during the occurrence of fault in the power transmission-line. The technique proposed in this paper, is verified using MATLAB/Simulink software and the obtained results shows that the wavelet based MRA is a good tool for detection and classification of faults. However it is also shown that the most critical problem related to this technique is the selection of appropriate threshold values for all the three phases. It has been also shown that this technique requires expert hands and knowledge of the system for the selection of proper threshold value.

Identification of Faults on Transmission Lines using Wavelet Technique

2017

The aim of this paper is to understand wavelet technique and identification of fault using wavelet transform. This paper presents high speed fault identification and protection of power system lines based on wavelet transform technique. It is a unique method which is used to detect the location and identification of fault in power system. Faults in power system are single line to ground, double line to ground and three phase faults. In this paper the conventional timeamplitude response is presented and the result shows that wavelet leads to identify the type of fault and its location. The results indicate that wavelet technique is fast compared to time amplitude technique. Keywords—Wavelet, Single Line to Ground fault, Double Line to Ground fault, Three phase fault.