scholarly journals Location of Faults In Transmission Line Using Fast Fourier Transform And Discrete Wavelet Transform In Power Systems

2012 ◽  
pp. 84-86
Author(s):  
Roshni Uppala ◽  
V. Niranjan ◽  
Ch. Das Prakash ◽  
R. Srinivas Rao
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Power Systems ◽  
Transmission Line ◽  
Fast Fourier Transform ◽  
Transmission Lines ◽  
High Signal ◽  
Discrete Wavelet ◽  
Matlab Simulation ◽  
Proposed Model

This paper demonstrates the usage of fast fourier transform and wavelet transform in locating faults using a simple transmission line. Transmission lines connect the generating stations and load centers. Hence, the chances of fault occurring in transmission lines are very high. Signal processing is the most important part of the digital distance protection schemes. The proposed model effectively helps in locating the fault such as L-G,LL,LL-G,LLL using MATLAB/SIMULINK. In doing so it describes the method of analysis of above two transforms in SIMULINK environment using the above two transforms. MATLAB simulation results show the wavelet method of transforms is a good and powerful tool to estimate the disrupts location on the transmission line when fault occurs.

scholarly journals Fault location identification of double circuit transmission line using discrete wavelet transform

2019 ◽  
Vol 15 (3) ◽  
pp. 1356
Author(s):  
Abdul Hadi Bin Mustapha ◽  
R Hamdan ◽  
F. H. Mohd Noh ◽  
N. A. Zambri ◽  
M. H. A. Jalil ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Transmission Line ◽  
Fast Fourier Transform ◽  
Discrete Wavelet Transform ◽  
Fault Location ◽  
Detection Algorithm ◽  
Percentage Error ◽  
Discrete Wavelet ◽  
Fast Fourier Transform Algorithm

<span lang="EN-GB">The importance of supplying undisturbed electricity keep increasing due to modernization and lifestyle. Any disturbance in the power system may lead to discontinuation and degradation in the power quality. Therefore, detecting fault, fault type and fault location is a major issue in power transmission system in order to ensure reliable power delivery system. This paper will compare two prominent methods to estimate the fault location of double circuit transmission line. Those methods are Discrete Wavelet Transform algorithm and Fast Fourier Transform algorithm. Simulations has been carried out in MATLAB/Simulink and a variety of fault has been imposed in order to analyse the capability and accuracy of the fault location detection algorithm. Results obtained portrayed that both algorithms provide good performance in estimating the fault location. However, the maximum percentage error produced by the Discrete Wavelet Transform is only 0.25%, 0.6% lower than maximum error produces by Fast Fourier Transform algorithm. As a conclusion, Discrete Wavelet Transform possesses better capability to estimate fault location as compared to Fast Fourier Transform algorithm.</span>

scholarly journals A Discrete Wavelet Transform based STATCOM Compensated Transmission Line

2018 ◽  
Vol 7 (4.5) ◽  
pp. 61
Author(s):  
S. K Mishra ◽  
L. N Tripathy ◽  
S. C Swain
Keyword(s):  
Wavelet Transform ◽  
Transmission Line ◽  
Discrete Wavelet Transform ◽  
Transmission Lines ◽  
Reactive Power ◽  
Spectral Energy ◽  
Discrete Wavelet ◽  
Current Signal ◽  
Static Synchronous Compensator ◽  
Phase Current

This paper describes the Discrete Wavelet transform (DWT) applied for classification and detection of fault with respect to the position of the Static Synchronous Compensator (STATCOM).  STATCOM is positioned at mid-point of the transmission line. The reactive power injection or absorption (i.e shifting phase voltage with respect to bus voltage) depends upon the apparent impedance during the fault. The current signals of each phase are retrieved from both sending and receiving end of three phase transmission line synchronously. It starts processing through DWT to acquire spectral energy (SE) content of each phase current signals from both end of the transmission line. Differential spectral energy (DSE) of each phase current signals (i.e SE of current signal recovered from sending end minus SE of current signal recovered at receiving end) of transmission lines is used to record the fault configuration. 

scholarly journals An Approach on MCSA-Based Fault Detection Using Discrete Wavelet Transform and Fault Classification Based on Deep Neural Networks

2021 ◽  
Vol 10 (3) ◽  
pp. 2256-2259
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Fast Fourier Transform ◽  
Discrete Wavelet Transform ◽  
Three Dimensional ◽  
Fault Classification ◽  
Signal Frequency ◽  
Discrete Wavelet ◽  
Novel Approach ◽  
Motor Current Signature Analysis

This paper presents a novel approach on motor current signature analysis (MCSA) forbroken Rotor Bar fault and High Contact Resistance fault using stator current signals as an input from the three phases of Induction motors. Discrete Wavelet Transform is preferred over the Fast Fourier Transform (FFT). Fast Fourier Transform (FFT) converts signals from time domain to frequency domain on the other hand Discrete Wavelet Transform (DWT) gives complete three-dimensional information of the signal, frequency, amplitude, and the time where the frequency components exist. In wavelet analysis, thesignal is converted into scaled and translated version of mother wavelet, which is very irregular so cannot be predicted. Hence, mother wavelets are more appropriate for predicting the local behavior of the signal including irregularities and spikes. In this research features are extracted using DWT and then features are trained in Deep NN sequential model for the purpose of classification of the faults. In this research, MATLAB software has been used for building the motor model in Simulink environment and PyCharm software is used to implement Deep NN for getting accuracy and classification results. This research helps in early detection of the faults that assists in prevention from unscheduled downtimes in industry, economy loss and production loss as well.

scholarly journals Separation of the Structure Signal by the Maximal Overlap Discrete Wavelet Transform and Fast Fourier Transform

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Thanh Q. Nguyen
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Fast Fourier Transform ◽  
Discrete Wavelet Transform ◽  
Power Spectra ◽  
Vibration Signal ◽  
Discrete Wavelet ◽  
Single Spectrum ◽  
Power Spectral ◽  
Vibration Process

Power spectral density (PSD) is used for evaluating a structure’s vibration process. Moreover, PSD not only shows a discrete form of vibration but also presents various components in a vibration structure. The superposition of multiple vibration patterns on the same spectrum poses difficulty in analyzing the spectral information in the way needed to find the structure’s discrete vibration. This paper proposes a method for separating the synthetic vibration signal into a structure’s discrete vibration and other extraneous vibrations using the maximal overlap discrete wavelet transform (MODWT) method combined with the method of fast Fourier transform (FFT). With the combination of these two algorithms, MODWT and FFT, the signals of the synthesized vibration have been separated into component signals with different frequency ranges. This means that PSD will be formed, which is based on the combination of the single power spectra of the component signals. Thus, the single spectrum of each of these constructed components can be used to evaluate the types of discrete vibrations coexisting in a structure’s vibration process. The survey results in this paper show the sensitivity and suitability of select types of discrete vibrations to be separated out during the assessment of the structural change, so as to achieve the best possible plan for eliminating the unwanted and unexpected noise and vibration components.

scholarly journals Computation of Wavelet and Multiwavelet Transforms Using Fast Fourier Transform

2021 ◽  
Vol 4 (2) ◽  
pp. 102-108
Author(s):  
Walid Amin Mahmoud
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Fast Fourier Transform ◽  
Haar Wavelet ◽  
Discrete Wavelet ◽  
Haar Wavelet Transform ◽  
Linear Convolution ◽  
Multiwavelet Transform ◽  
Discrete Multiwavelet Transform ◽  
Accuracy Tradeoff

A novel fast and efficient algorithm was proposed that uses the Fast Fourier Transform (FFT) as a tool to compute the Discrete Wavelet Transform (DWT) and Discrete Multiwavelet Transform. The Haar Wavelet Transform and the GHM system are shown to be a special case of the proposed algorithm, where the discrete linear convolution will adapt to achieve the desired approximation and detail coefficients. Assuming that no intermediate coefficients are canceled and no approximations are made, the algorithm will give the exact solution. Hence the proposed algorithm provides an efficient complexity verses accuracy tradeoff.   The main advantages of the proposed algorithm is that high band and the low band coefficients can be exploited for several classes of signals resulting in very low computation.

scholarly journals Study of Transmission Line Boundary Protection Using a Multilayer Perceptron Neural Network with Back Propagation and Wavelet Transform

2021 ◽  
Vol 4 (4) ◽  
pp. 95
Author(s):  
Daniel Okojie ◽  
Linus Idoko ◽  
Daniel Herbert ◽  
Agha Nnachi
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Power Systems ◽  
Transmission Line ◽  
Transmission Lines ◽  
Multilayer Perceptron ◽  
Wavelet Transforms ◽  
Back Propagation ◽  
Transient Signal ◽  
Research Approach

Protection schemes are usually implemented in the planning of transmission line operations. These schemes are expected to protect not only the network of transmission lines but also the entire power systems network during fault conditions. However, it is often a challenge for these schemes to differentiate accurately between various fault locations. This study analyses the deficiencies identified in existing protection schemes and investigates a different method that proposes to overcome these shortcomings. The proposed scheme operates by performing a wavelet transform on the fault-generated signal, which reduces the signal into frequency components. These components are then used as the input data for a multilayer perceptron neural network with backpropagation that can classify between different fault locations in the system. The study uses the transient signal generated during fault conditions to identify faults. The scientific research paradigm was adopted for the study. It also adopted the deduction research approach as it requires data collection via simulation using the Simscape electrical sub-program of Simulink within Matrix laboratory (MATLAB). The outcome of the study shows that the simulation correctly classifies 70.59% of the faults when tested. This implies that the majority of the faults can be detected and accurately isolated using boundary protection of transmission lines with the help of wavelet transforms and a neural network. The outcome also shows that more accurate fault identification and classification are achievable by using neural network than by the conventional system currently in use.

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