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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Mohammad Nizam Ibrahim
Universiti Teknologi MARA

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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.
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%).
Uncertainty and sensitivity analysis applied on commercial tariff with off-peak tariff rider: a case study Mohammad Nizam Ibrahim; Anuar Mohamad; Zainol Asri Abdul Sani @ Salleh; Mohd Muzafa Jumidali; Azahar Taib
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i3.pp1176-1184

Abstract

Commercial Tariff with Off-Peak Tariff Rider (C1 OPTR) is one type of time-based electricity tariff. The C1 OPTR charges electricity consumers with different electricity rates instead of a flat rate tariff. This paper investigates the C1 OPTR tariff adopted recently by Universiti Teknologi MARA Cawangan Pulau Pinang (UiTMCPP) from its previous flat rate tariff. The investigation involves applying the uncertainty and sensitivity analysis to the average load factor (ALF) model of the UiTMCPP. The ALF model consists of two major factors, namely kilowatt-hour (kWh) and maximum demand (kW). The analysis aims to identify the most contributing factor between the kWh and kW to the uncertainty of the ALF in a systematic way using Monte Carlo simulation. The factor identified is important for improvement by UiTMCPP to ensure that the suitable target ALF can be easily achieved. Based on Sobol uncertainty and sensitivity analysis technique, 60,000 samples for the respective kWh and kW have been generated and executed to produce the output of the ALF model. The result of the uncertainty analysis shows that the ALF output is uncertain between 0.195 and 0.343. Furthermore, the applied sensitivity analysis discovers that the kW is the most contributing factor to the ALF output uncertainty, with the sensitivity index indicating 0.8853.
Co-Authors Abdul Malek Saidina Omar Ahmad Asri Abd Samat Ahmad Farid Abidin Anuar Mohamad Azahar Taib Mimi Nurzilah Hashim Mohd Muzafa Jumidali Muhammad Khusairi Osman Zainol Asri Abdul Sani @ Salleh Zakaria Hussain