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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
H. A. Hamid
Universiti Malaysia Perlis (UniMAP)
Electrical & Electronics Engineering

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Modelling and simulation of online partial discharge measurement for medium voltage power cable A. Z. Abdullah; M. Isa; M. N. K. H. Rohani; H. A. Hamid; M. H. Amlus; N. Azizan
Bulletin of Electrical Engineering and Informatics Vol 9, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1223.385 KB) | DOI: 10.11591/eei.v9i2.2076

Abstract

This paper presents the modelling of the online partial discharge (PD) measurement of the medium voltage (MV) power cable. Recently, PD monitoring trends are rapidly increasing due to high demand on reliable systems. Degradation are mainly due to the presence of PD in the high voltage power equipment used. PD measurement is therefore a highly recommended task to early detection of the degradation insulation for high voltage (HV) equipment in order to avoid breakdowns. Real network modelling is necessary to improvise system design in order to find the efficiency in a real power system network. In this paper, modelling focuses on a real distribution network by applying Rogowski coil (RC) as a detection sensor to trigger PD activity. The simulation is performed to determine the functionality and reliability of the system with the RC application in the network. The analysis is performed in the ATP-EMTP and MATLAB Simulink software environments. In addition, this paper contributed to justify the approach of a simplified PD sensor and measurement system. This PD measurement system provides a complete solution in the context of condition-oriented monitoring for the ability to apply the RC to trigger PD activity in the power distribution network. 
An alternative approaches to predict flashover voltage on polluted outdoor insulators using artificial intelligence techniques Ali. A. Salem; Rahisham Abd Rahman; M. S. Kamarudin; N. A. Othman; N. A. M. Jamail; H. A. Hamid; M. T. Ishak
Bulletin of Electrical Engineering and Informatics Vol 9, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (827.369 KB) | DOI: 10.11591/eei.v9i2.1864

Abstract

This paper presents an alternative approach for predicting critical voltage of pollution flashover by using Artificial Intelligence (AI) technique. Data from experimental works combined with the theoretical results from well-known theoretical modelling are used to derive algorithm for Artificial Neural Network (ANN) and Adaptive Neuro-fuzzy Inference System (ANFIS) for determining critical voltage of flashover. Series of laboratory testing and measurement are carried for 1:1, 1:5 and 1:10 ratios of top to bottom surface salt deposit density on cup and pin insulators. Insulators variables such as height H, diameter D, form factor F, creepage distance L, equivalent salt deposit density (ESDD) and flashover voltage correction are identified and used to train the AI network. Comparative studies have evidently shown that the proposed (AI) technique gives the satisfactory results compared to the analytical model and test data with the Coefficient of determination R-Square value of more than 97%.
Wavelet based de-noising for on-site partial discharge measurement signal A. Z. Abdullah; M. Isa; S. N. M. Arshad; M. N. K. H. Rohani; H. S. A. Halim; A. N. Nanyan; H. A. Hamid
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 1: October 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i1.pp259-266

Abstract

This paper presents, wavelet based de-noising technique for on-site partial discharge (PD) measurement signal. The signal is measured from medium voltage power cable at 11 kV distribution substation. The best mother wavelet, decomposition level and the type of threshold for the de-noising technique are selected based on the signal to noise ratio (SNR) aggregation. The SNR aggregation is determined based on the minimum, maximum, mean and standard deviation parameters.   The same standard de-noising procedure is applied for two different PD signals and the selection parameters are done based on the accuracy of de-noising analysis. The analysis is performed in MATLAB software environment and Daubechies 2 (db2) is found as the best mother wavelet at tenth decomposition levels with soft threshold type. This study is specifically performed to develop the de-noising procedure for on-site PD measurement. Overall results indicate that the right selection of the de-noising procedure will help to improve the PD signal detection from on–site measurement.
Co-Authors A. N. Nanyan A. Z. Abdullah Ali. A. Salem H. S. A. Halim M. H. Amlus M. Isa M. N. K. H. Rohani M. S. Kamarudin M. T. Ishak N. A. M. Jamail N. A. Othman N. Azizan Rahisham Abd Rahman S. N. M. Arshad