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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Ahmad Farid Abidin
Universiti Teknologi Mara

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Journal : Indonesian Journal of Electrical Engineering and Computer Science

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Fault classification on transmission line using LSTM network Abdul Malek Saidina Omar; Muhammad Khusairi Osman; Mohammad Nizam Ibrahim; Zakaria Hussain; Ahmad Farid Abidin
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 1: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i1.pp231-238

Abstract

Deep Learning has ignited great international attention in modern artificial intelligence techniques. The method has been widely applied in many power system applications and produced promising results. A few attempts have been made to classify fault on transmission lines using various deep learning methods. However, a type of deep learning called long short-term memory (LSTM) has not been reported in literature. Therefore, this paper presents fault classification on transmission line using LSTM network as a tool to classify different types of faults. In this study, a transmission line model with 400 kV and 100 km distance was modelled. Fault free and 10 types of fault signals are generated from the transmission line model. Fault signals are pre-processed by extracting post-fault current signals. Then, these signals are fed as input to the LSTM network and trained to classify 10 types of faults. The white Gaussian noise of level 20 dB and 30 dB signal to noise ratio (SNR) is also added to the fault current signals to evaluate the immunity of the proposed model. Simulation results show promising classification accuracy of 100%, 99.77% and 99.55% for ideal, 30 dB and 20 dB noise respectively. Results has been compared to four different methods which can be seen that the LSTM leading with the highest classification accuracy. In line with the purpose of the LSTM functions, it can be concluded that the method has a capability to classify fault signals with high accuracy.
Detection of fault during power swing in test system interconnected with DG Nor Zulaily Mohamad; Ahmad Farid Abidin; Ismail Musirin
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp577-585

Abstract

Distance relay is prone to mal-operate during power swing, thus most of modern distance relay design is equipped with power swing blocking scheme to block the operation during power swing and reset the blocking operation whenever a fault occurs during power swing. However, the detection of fault during power swing especially for high resistance fault possess a challenging task, therefore it may cause the unblocking function to vulnerable to operate. This paper presents the development of a detection scheme for detecting fault during power swing in test system interconnected with Distributed Generation (DG). In this study, the detection scheme is proposed based on S-Transform analysis on the distance relay input voltage signal. It is demonstrated that the proposed S-Transform detection based scheme can effectively detect various type of fault during power swing includes high resistance fault, as well as able to operate correctly even with the presence of DG in the test system.
A Comparison Study of Learning Algorithms for Estimating Fault Location Mimi Nurzilah Hashim; Muhammad Khusairi Osman; Mohammad Nizam Ibrahim; Ahmad Farid Abidin; Ahmad Asri Abd Samat
Indonesian Journal of Electrical Engineering and Computer Science Vol 6, No 2: May 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v6.i2.pp464-472

Abstract

Fault location is one of the important scheme in power system protection to locate the exact location of disturbance. Nowadays, artificial neural networks (ANNs) are being used significantly to identify exact fault location on transmission lines. Selection of suitable training algorithm is important in analysis of ANN performance. This paper presents a comparative study of various ANN training algorithm to perform fault location scheme in transmission lines. The features selected into ANN is the time of first peak changes in discrete wavelet transform (DWT) signal by using faulted current signal acted as traveling wave fault location technique. Six types commonly used backpropagation training algorithm were selected including the Levenberg-Marquardt, Bayesian Regulation, Conjugate gradient backpropagation with Powell-Beale restarts, BFGS quasi-Newton, Conjugate gradient backpropagation with Polak-Ribiere updates and Conjugate gradient backpropagation with Fletcher-Reeves updates. The proposed fault location method is tested with varying fault location, fault types, fault resistance and inception angle. The performance of each training algorithm is evaluated by goodness-of-fit (R2), mean square error (MSE) and Percentage prediction error (PPE). Simulation results show that the best of training algorithm for estimating fault location is Bayesian Regulation (R2 = 1.0, MSE = 0.034557 and PPE = 0.014%).
Co-Authors Abdul Malek Saidina Omar Ahmad Asri Abd Samat Ismail Musirin Mimi Nurzilah Hashim Mohammad Nizam Ibrahim Muhammad Khusairi Osman Nor Zulaily Mohamad Zakaria Hussain