Transmission Line Fault Detection, Classification and Location using Wavelet Transform

Abstract

This paper present a new algorithm for fault detection, classification and location of overhead transmission line using Wavelet Transform (WT) based Discrete Wavelet Transform (DWT) is proposed. The different system faults such as LG, LLG and LLLG in transmission line should be detect, classify and locate rapidly. The proposed method is based on the voltage and current signal information from the power model in MATLAB to generate the transient voltage and current signal in both time and frequency domain. DWT using “db6” as a mother wavelet is used to capture transient current signals and extract the high frequency detail coefficient for detecting and classifying the fault disturbance. The classification process is based on ground threshold value. The location of faults is carried out by obtaining the fault information from source terminal end to remote terminal end along with the total transmission line length. The proposed method is tested on the MATLAB/SIMULINK environment with the Simulink model. In simulation process the proposed algorithm achieved the fault detection, classification and locate all eleven types of possible fault in transmission line and the result are compared with the AR and MED method.

Disturbance Modeling on Transmission Lines Using Wavelet Transform Method: A Review

“…There are 2 methods for locating the disturbance, which is Impedance -Based Method [1]. and Traveling Wave (TW) -Based Method [1], [2]. The method also can be classified by its fault location as a single-end method and a double-end method [4].…”

“…Several fault detection and classification techniques have been experimented with. They are wavelet transform [2]- [5], fuzzy logic [29], [30], artificial neural network (ANN) [8], support vector machines (SVM) [9], [10], WT, and ANN [13], WT and fuzzy logic [26] and combination of ANN and fuzzy logic [13], [37], [38].…”

“…Bewley first introduced the analysis techniques of traveling waves (TW) in the 1930s [2]. Meanwhile, in the 1960s Barthold and carter came along with their digital solutions [3].…”

“…In the application of continuous wavelet transform (CWT), it produces a lot of wavelet transformation coefficients, causing the resulting data to become redundant [1], [7], [8], [22]. This problem can be overcome by using a discrete wavelet transform (DWT), where the decomposition algorithm uses MATLAB, which is the basis of the DWT system, by separating (decomposing) two input waveform signals into a low frequency (low frequency), which is called approximation, and a high frequency (high frequency), which is called detail [2], [23].…”

Shunt faults detection and classification in electrical power transmission line systems based on artificial neural networks

“…To maintain stability and prevent damage to electrical power transmission line devices, these faults must be detected quickly, classified and cleared within a particular time (Sharma et al , 2017). There are several methods for fault detection and classification such as wavelet transform (WT) (Balakrishnan and Sathiyasekar, 2019), artificial neural network (ANN) (Fuada et al , 2020; Upadhyay et al , 2018), fuzzy logic (Bhatnagar and Yadav, 2020), adaptive neuro-fuzzy inference System (Lirouana and Mohammed, 2021), concurrent neuro-fuzzy (Eboule et al , 2018), support vector machine (Coban and Tezcan, 2021), WT and ANN (Gowrishankar et al , 2016; Thwe and Oo, 2016), WT and fuzzy logic (Ray et al , 2016), and there are also various computational models of the ANN that have been used in transmission line system fault detection and classification, such as multi-layer perceptron neural network (MLPNN) (Okojie et al , 2021), Elman recurrent neural network (ERNN) (Aborisade et al , 2021), WT and ERNN (Zakri and Tua, 2020), and radial basis function neural network (RBFNN) (Gupta and Mahanty, 2015). In this study, an ANN was employed for its capability to detect and classify faults in power transmission line systems.…”