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.

Fault Location of Transmission Line Based on Discrete Wavelet Transforms

2011 ◽  
Vol 121-126 ◽  
pp. 1269-1273
Author(s):  
Wen Xiu Tang ◽  
Mo Zhang ◽  
Ying Liu ◽  
Xu Fei Lang ◽  
Liang Kuan Zhu
Keyword(s):  
Transmission Line ◽  
Transmission Lines ◽  
Wavelet Transforms ◽  
Fault Location ◽  
Short Circuit ◽  
Signal Transmission ◽  
Transient Signal ◽  
Discrete Wavelet ◽  
Novel Method ◽  
Non Stationary Signal

In this paper, a novel method is investigated to detect short-circuit fault signal transmission lines in strong noise environment based on discrete wavelet transform theory. Simulation results show that the method can accurately determine the fault position, can effectively analyze the non-stationary signal and be suitable for transmission line fault occurred after transient signal detection. Furthermore, it can effectively eliminate noise effects of fault signal so as to realize the transmission lines of accurate fault.

Transmission Line Using Discrete Wavelet Transform and Back-Propagation Neural Network Based on Clarke’s Transformation

2016 ◽  
Vol 818 ◽  
pp. 156-165 ◽  
Author(s):  
Makmur Saini ◽  
Abdullah Asuhaimi bin Mohd Zin ◽  
Mohd Wazir Bin Mustafa ◽  
Ahmad Rizal Sultan ◽  
Rahimuddin
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Transmission Line ◽  
Discrete Wavelet Transform ◽  
Back Propagation ◽  
Absolute Error ◽  
Back Propagation Neural Network ◽  
Fault Classification ◽  
Discrete Wavelet ◽  
Comparison Of The Results

This paper proposes a new technique of using discrete wavelet transform (DWT) and back-propagation neural network (BPNN) based on Clarke’s transformation for fault classification and detection on a single circuit transmission line. Simulation and training process for the neural network are done by using PSCAD / EMTDC and MATLAB. Daubechies4 mother wavelet (DB4) is used to decompose the high frequency components of these signals. The wavelet transform coefficients (WTC) and wavelet energy coefficients (WEC) for classification fault and detect patterns used as input for neural network training back-propagation (BPNN). This information is then fed into a neural network to classify the fault condition. A DWT with quasi optimal performance for preprocessing stage are presented. This study also includes a comparison of the results of training BPPN and DWT with and without Clarke’s transformation, where the results show that using Clarke transformation in training will give in a smaller mean square error (MSE) and mean absolute error (MAE). The simulation also shows that the new algorithm is more reliable and accurate.

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 WT-ANN based fault discrimination for transmission system

2018 ◽  
Vol 7 (1.8) ◽  
pp. 144
Author(s):  
P Venkata Lakshmi ◽  
P N. S. Poojitha ◽  
Y Srinivasrao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Power System ◽  
Transmission Line ◽  
Transmission Lines ◽  
Power Supply ◽  
Protection Scheme ◽  
Efficient Protection ◽  
Continuous Power

Protection of transmission line is a complex in power system as the majority of the faults in power system are transmission faults. A proper protection is needed for transmission line for continuous power supply. To provide a strong as well as an efficient protection scheme, in this paper we are using wavelet technique and artificial neural network. By using these mentioned two techniques we can detect the faults in transmission line and also, we can classify the detected faults. Wavelet transform has strong mathematical, very fast and accurate tools for brief signal inside the transmission lines and synthetic neural network can make a unique between measured sign and associated signal that has different pattern. 

scholarly journals Complementary Feature Extractions for Event Identification in Power Systems Using Multi-Channel Convolutional Neural Network

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4446
Author(s):  
Do-In Kim
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Convolutional Neural Network ◽  
Time Window ◽  
Transient Signal ◽  
Measurement Unit ◽  
Transient Phenomena ◽  
Event Identification ◽  
Starting Point ◽  
Fast Decision

This paper presents an event identification process in complementary feature extractions via convolutional neural network (CNN)-based event classification. The CNN is a suitable deep learning technique for addressing the two-dimensional power system data as it directly derives information from a measurement signal database instead of modeling transient phenomena, where the measured synchrophasor data in the power systems are allocated by time and space domains. The dynamic signatures in phasor measurement unit (PMU) signals are analyzed based on the starting point of the subtransient signals, as well as the fluctuation signature in the transient signal. For fast decision and protective operations, the use of narrow band time window is recommended to reduce the acquisition delay, where a wide time window provides high accuracy due to the use of large amounts of data. In this study, two separate data preprocessing methods and multichannel CNN structures are constructed to provide validation, as well as the fast decision in successive event conditions. The decision result includes information pertaining to various event types and locations based on various time delays for the protective operation. Finally, this work verifies the event identification method through a case study and analyzes the effects of successive events in addition to classification accuracy.

Dual-mode dendritic devices enhanced neural network based on electrolyte gated transistors

2021 ◽  
Author(s):  
Zhaokun Jing ◽  
Yuchao Yang ◽  
Ru Huang
Keyword(s):  
Neural Network ◽  
Multilayer Perceptron ◽  
Back Propagation ◽  
Input Voltage ◽  
High Signal ◽  
Neuron Models ◽  
Dual Mode ◽  
Dendritic Processing ◽  
The Neural Networks ◽  
Processing Block

Abstract As a fundamental component of biological neurons, dendrites have been proven to have crucial effects in neuronal activities. Single neurons with dendrite structures show high signal processing capability that is analogous to a multilayer perceptron, whereas oversimplified point neuron models are still prevalent in AI algorithms and neuromorphic systems and fundamentally limit their efficiency and functionality of the systems constructed. In this study, we propose a dual-mode dendritic device based on electrolyte gated transistor, which can be operated to generate both supralinear and sublinear current-voltage responses when receiving input voltage pulses. We propose and demonstrate that the dual-mode dendritic devices can be used as a dendritic processing block between weight matrices and output neurons so as to enhance the expression ability of the neural networks. A dual-mode dendrites-enhanced neural network is therefore constructed with only two trainable parameters in the second layer, thus achieving 1000× reduction in the amount of second layer parameter compared to multilayer perceptron. After training by back propagation, the network reaches 90.1% accuracy in MNIST handwritten digits classification, showing advantage of the present dual-mode dendritic devices in building highly efficient neuromorphic computing.

scholarly journals Classification and direction discrimination of faults in transmission lines using 1D convolutional neural networks

2021 ◽  
Vol 12 (3) ◽  
pp. 1928
Author(s):  
Ahmed Thamer Radhi ◽  
Wael Hussein Zayer ◽  
Adel Manaa Dakhil
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Transmission Line ◽  
Convolutional Neural Networks ◽  
Transmission Lines ◽  
Fault Classification ◽  
Line System ◽  
Direction Discrimination ◽  
Conventional Methods

<span lang="EN-US">This paper presents a fast and accurate fault detection, classification and direction discrimination algorithm of transmission lines using one-dimensional convolutional neural networks (1D-CNNs) that have ingrained adaptive model to avoid the feature extraction difficulties and fault classification into one learning algorithm. A proposed algorithm is directly usable with raw data and this deletes the need of a discrete feature extraction method resulting in more effective protective system. The proposed approach based on the three-phase voltages and currents signals of one end at the relay location in the transmission line system are taken as input to the proposed 1D-CNN algorithm. A 132kV power transmission line is simulated by Matlab simulink to prepare the training and testing data for the proposed 1D- CNN algorithm. The testing accuracy of the proposed algorithm is compared with other two conventional methods which are neural network and fuzzy neural network. The results of test explain that the new proposed detection system is efficient and fast for classifying and direction discrimination of fault in transmission line with high accuracy as compared with other conventional methods under various conditions of faults.</span>

Sign in / Sign up

Export Citation Format

Share Document