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267 results on '"Illias, Hazlee A."'

52. Planar Sensor for Noise Cancellation During Partial Discharge Detection in Open Substation

Author

Azam, S. M. Kayser, Othman, Mohamadariff, Illias, Hazlee Azil, Latef, Tarik Abdul, Mahadi, Wan Nor Liza Wan, Fahmi, Daniar, Hossain, A. K. M. Zakir, and Ain, Mohd Fadzil

Abstract

In compliance with next-generation high-voltage substations, ultrahigh-frequency (UHF) planar sensors are becoming more popular than ever for their online features in partial discharge (PD) diagnostics. However, PD signals mixing with different noises still exist as a major challenge for UHF sensors. Especially in open substations, telecommunication interferences become predominant noise sources to an external UHF sensor placed outside of the equipment chamber. Even though band-stop filters are additionally connected to UHF sensors for denoising, the inherent noise cancellation mechanism is not yet found in any UHF sensor. With the best of our knowledge and survey, we are the first to introduce here a planar sensor that inherently eliminates telecommunication interferences during PD detection (PDD) for open substations even without using any postprocessing filters. To design our sensor, we have applied techniques of truncated partial ground plane for wideband characteristics and modified radiating patch for enhanced gain performance. A novel intermingled coupling resonator is designed to remove the GSM-900 band from within the sensor bandwidth. We have tested our prototype both for free-space propagation and PDD performances. The prototype has a compact physical size of $16\times $ 8 cm2 and an operational bandwidth of 792–1414 MHz. For a surface discharge source, the prototype successfully detects the PD signals at the earliest stage. Our prototype minimizes the ambient noise floor to 2.5 mV and improves the signal-to-noise ratio by more than 27.9 dB, which is relatively higher than conventional UHF sensors. The proposed sensor also has the potential of classifying PD sources.

Published
2023
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63. Classification and regression analysis using support vector machine for classifying and locating faults in a distribution system

Author

GURURAJAPATHY, Sophi Shilpa, MOKHLIS, Hazlie, and ILLIAS, Hazlee Azil Bin

Subjects
Support vector machine,artificial neural network,deep neural network,extreme learning machine,kriging,fault location,power distribution,electrical power, Hardware_PERFORMANCEANDRELIABILITY
Abstract

Various fault location methods have been developed in the past to identify the faulty phase, fault type, faulty section, and distance. However, this identification is commonly conducted in a separate manner. An effective fault location should be able to identify all of these at the same time. Therefore, in this work, a method using a support vector machine (SVM) to identify the fault type, faulty section, and distance considering the faulty phase is proposed. The proposed method uses voltage sag magnitude of the distribution system as the main feature for the SVM to identify faults. The fault type is classified using a directed acyclic graph SVM. The possible faulty sections are identified by estimating the fault resistance using support vector regression and matching the voltage sag data in the database with the actual voltage sag data. The most possible faulty sections are ranked using ranking analysis. The fault distance for the possible faulty sections is then identified using support vector regression analysis and its overfitting or underfitting issues are addressed by the proper selection of a regularization parameter. The feasibility of the proposed method was tested on an actual Malaysian distribution system. The results of faulty phase, fault type, faulty section, and fault distance are analyzed. The performance of the proposed method is compared with various other intelligent methods such as the artificial neural network, deep neural network, extreme learning machine, and kriging method. The test results indicate that the faulty phase and fault type yield 100 % accurate results. All the faulty sections are identified and the proposed method obtains reliable fault location.

Published
2018

64. Parametric Evaluation of 11kV Zinc Oxide Surge Arrester using Finite Element Analysis Model

Author

A. A. Latiff Nurul, Z. A. Dabbak Sameh, Ab H. A. Bakar, and A. Illias Hazlee

Subjects
Materials science, chemistry, Surge arrester, business.industry, chemistry.chemical_element, Structural engineering, Zinc, business, Finite element method, Parametric statistics
Abstract

A surge arrester on transmission line is used to deflect lightning surge to ground, avoiding power systems from destruction. During normal condition, there is a leakage current flow through surge arresters where it is one of the main causes of surge arrester humiliation. Thus, monitoring the surge arrester condition is very significant. In this work, the effect of several parameters in a 11kV zinc oxide surge arrester design based on leakage current distribution has been studied. A comparison is made between measurements and modelling in order to validate the model that has been developed using finite element analysis. This work found that the benefit of using FEA-based model where the design parameters that affect the leakage current can be identified. Through this study, a well knowledge of leakage current can be achieved and also help in zinc oxide surge arrester design.

Published
2021

65. Bio‐based Poly(hydroxy urethane)s: Synthesis and Pre/Post‐Functionalization.

Author

Mhd. Haniffa, Mhd. Abd. Cader, Munawar, Khadija, Ching, Yern Chee, Illias, Hazlee Azil, and Chuah, Cheng Hock

Subjects
URETHANE, BINDING agents, HYDROXYL group, COMPLEX organizations, MARKET value, POLYURETHANE elastomers
Abstract

New and emerging demand for polyurethane (PU) continues to rise over the years. The harmful isocyanate binding agents and their integrated PU products are at the height of environmental concerns, in particular PU (macro and micro) pollution and their degradation problems. Non‐isocyanate poly(hydroxy urethane)s (NIPUs) are sustainable and green alternatives to conventional PUs. Since the introduction of NIPU in 1957, the market value of NIPU and its hybridized materials has increased exponentially in 2019 and is expected to continue to rise in the coming years. The secondary hydroxyl groups of these NIPU′s urethane moiety have revolutionized them by allowing for adequate pre/post functionalization. This minireview highlights different strategies and advances in pre/post‐functionalization used in biobased NIPU. We have performed a comprehensive evaluation of the development of new ideas in this field to achieve more efficient synthetic biobased hybridized NIPU processes through selective and kinetic understanding. [ABSTRACT FROM AUTHOR]

Published
2021
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74. A New Approach for Estimating Insulation Condition of Field Transformers Using FRA.

Author

MOHD YOUSOF, Mohd Fairouz, AL-AMERI, Salem, AHMAD, Hussein, ILLIAS, Hazlee Azil, and ARSHAD, Syahrun Nizam Md

Subjects
STATISTICS, POWER transformers
Abstract

Frequency response analysis (FRA) is a tool for evaluating mechanical integrity of transformer's core and winding. However, several studies have reported that FRA is also sensitive to other parameters such as temperature, moisture content and presence of oil. Since some of these parameters are critical in the insulation degradation process, it is interesting to further investigate the possibility of using FRA for insulation assessment. In this paper, the insulation conditions of three field transformers are investigated using FRA. This is performed after statistical analysis on the response suggested no mechanical damage on the transformers. The responses are further analyzed to determine the percentage of change of winding capacitance. This is achieved by comparing resonance frequencies between the responses. The percentage of change represents the amount of degradation in the insulation. This is because as the insulation condition degrades, the response is shifted towards lower frequencies. This new investigation demonstrates the applicability of FRA to estimate the insulation degradation. [ABSTRACT FROM AUTHOR]

Published
2020
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82. Artificial neural network application for partial discharge recognition: survey and future directions

Author

Mas'ud, Abdullahi Abubakar, Albarracín, Ricardo, Ardila-Rey, Jorge Alfredo, Muhammad-Sukki, Firdaus, Illias, Hazlee Azil, Bani, Nurul Aini, and Munir, Abu Bakar

Subjects
Partial discharge (PD), Artificial neural network (ANN), Artificial intelligence, AS Academies and learned societies (General)
Abstract

In order to investigate how artificial neural networks (ANNs) have been applied for partial discharge (PD) pattern recognition, this paper reviews recent progress made on ANN development for PD classification by a literature survey. Contributions from several authors have been presented and discussed. High recognition rate has been recorded for several PD faults, but there are still many factors that hinder correct recognition of PD by the ANN, such as high-amplitude noise or wide spectral content typical from industrial environments, trial and error approaches in determining an optimum ANN, multiple PD sources acting simultaneously, lack of comprehensive and up to date databank of PD faults, and the appropriate selection of the characteristics that allow a correct recognition of the type of source which are currently being addressed by researchers. Several suggestions for improvement are proposed by the authors include: (1) determining the optimum weights in training the ANN; (2) using PD data captured over long stressing period in training the ANN; (3) ANN recognizing different PD degradation levels; (4) using the same resolution sizes of the PD patterns when training and testing the ANN with different PD dataset; (5) understanding the characteristics of multiple concurrent PD faults and effectively recognizing them; and (6) developing techniques in order to shorten the training time for the ANN as applied for PD recognition Finally, this paper critically assesses the suitability of ANNs for both online and offline PD detections outlining the advantages to the practitioners in the field. It is possible for the ANNs to determine the stage of degradation of the PD, thereby giving an indication of the seriousness of the fault.

Published
2016

94. Fault location in an unbalanced distribution system using support vector classification and regression analysis.

Author

Gururajapathy, Sophi Shilpa, Mokhlis, Hazlie, Illias, Hazlee Azil Bin, Abu Bakar, Ab Halim, and Awalin, Lilik Jamilatul

Subjects
SUPPORT vector machines, DATA analysis, REGRESSION analysis, ELECTRIC potential, KERNEL (Mathematics)
Abstract

Support vector machine (SVM) is a novel machine for data analysis and has advantageous characteristic of good generalization. Because of this characteristic, SVM is used in this work for fault classification and diagnosis in distribution systems. This work proposes an effective fault location method using SVM to identify the fault type, faulty section, and fault distance. The classification and regression analysis of the SVM are performed to locate a fault. The proposed method utilizes the voltage sag magnitude and angle measured at the primary substation of a distribution system. First, the fault type is identified using one- versus-one concept of support vector classification. The next step identifies the faulty section by calculating fault resistance, finding possible faulty sections and ranking the possible sections. Finally, the fault distance is identified using support vector regression analysis. The performance of the proposed method is tested using SaskPower distribution system from Canada having 20 line sections. Test cases are carried out under various fault scenarios considering the fault type and fault resistance. The results of fault distance are compared for different kernel functions, and the most accurate kernel is chosen. Test results show that the proposed method obtains reliable fault location. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Published
2018
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