Search

Your search keyword '"Illias, Hazlee A."' showing total 147 results
Start Over Author "Illias, Hazlee A." Search Limiters Available in Library Collection
147 results on '"Illias, Hazlee A."'

10. Streamlining Fault Classification of Dissolved Gases in Transformer Using Data Synthesis and Dimension Reduction

Author

Kumar, Dhruba, Dutta, Saurabh, and Illias, Hazlee Azil

Abstract

This article addresses the challenge of limited data availability of dissolved gas analysis (DGA) in fault diagnosis of power transformers. While various classifiers have been employed for fault classification in the prior research, their accuracy depends heavily on balanced and synthetic training data. Although resampling techniques have been implemented to address that, their statistical validity remains unproven. Synthetic data generation is particularly crucial for imbalanced and scarce DGA fault samples. However, there is limited discussion on generating synthetic data for imbalanced datasets, which can negatively impact classifier training and machine learning (ML) models. This article introduces a novel approach for generating synthetic data to overcome the limited availability of fault data in power transformer diagnosis. The effectiveness of the generated data is ensured through ${z}$ -score normalization of the original gas concentration values. In addition, dimension reduction from 5-D to 2-D data is performed to simplify the data generation process. The proposed approach utilizes t-distributed stochastic neighbor embedding (t-SNE) as a superior dimension reduction technique, verified through reconstruction error and explained variance (Ev) metrics. The performance of the proposed generative adversarial network (GAN)-based approach is compared with four standard algorithms using multiple evaluation metrics. The results demonstrate that the GAN outperforms the other algorithms in generating synthetic data that closely resembles actual data. The proposed method achieves an accuracy of up to 98.10%, surpassing existing methods. This confirms the proposed GAN-based approach, in conjunction with t-SNE, effectively processes actual data for power transformer fault diagnosis, thus overcoming the limitations of limited data availability.

Published
2024
Full Text
View/download PDF

11. Optimizing Transformer Fault Detection Performance Through the Synergy of AI and Statistical Analysis for Multi-Fault Classification

Author

Kumar, Dhruba, Dutta, Saurabh, and Illias, Hazlee Azil

Abstract

The recent development of artificial intelligence (AI) has opened new avenues in processing parts per million (ppm) for fault detection through dissolved gas analysis (DGA). According to the latest IEC and IEEE standards, the existing methods are only applicable on single fault occurrence. The paper focuses on the challenge of detecting multiple faults occurring simultaneously in cases involving many faults using AI. Further, an inadequate training sample for classification and unavailability of balanced per-fault data reduces the model generalization, increases the risk of overfitting and biased learning towards the majority class. The proposed approach involves normalizing raw ppm values using z-score normalization, reducing dimensionality through t-distributed stochastic neighbor embedding (t-SNE), and synthesizing data using a generative adversarial network (GAN). Additionally, the parameters of error-correcting output codes (ECOC) and forest classifiers are optimized using a genetic algorithm (GA), efficiently solving multiple faults. F1 score, area under curve (AUC), and k-fold loss are used to evaluate fitness for improved classifier performance. This method outperforms the Duval method, and the data synthesis represents a new contribution to the field. The proposed method can achieve an overall accuracy of 99.6%, 98.6%, and 97.3% for the 9, 15, and 31 classes, respectively.

Published
2024
Full Text
View/download PDF

13. Measurement and simulation of partial discharges within a spherical cavity in a solid dielectric material

Author

Illias, Hazlee Azil, Chen, Guanghui, and Lewin, Paul

Subjects
530.412, QA75 Electronic computers. Computer science
Abstract

For high voltage components, the measurement of partial discharge (PD) is used in the performance assessment of an insulation system. Through modelling the PD process, a better understanding of the phenomenon may be attained. In this work, a model for a spherical cavity within a homogeneous dielectric material has been developed using Finite Element Analysis (FEA) software in parallel with MATLAB programming code. The model has been used to study the effect of various applied stresses and cavity conditions on PD activity and also the electric field and temperature distributions within the cavity. The experimental measurement of PD activity within a spherical cavity has also been undertaken. The measurements were performed for different amplitudes and frequencies of the applied voltage, a range of spherical cavity sizes and temperature variation of the material. The obtained results show that PD is strongly influenced by various conditions of the cavity and applied stress. The cycle to cycle behaviour of PD events, discharge phase and magnitude distributions, numbers of PDs per cycle, total charge magnitude per cycle, mean charge magnitude and maximum charge magnitude for each experiment have been obtained and analysed. The simulation results from the PD model have been compared with the measurement results. It is found that certain model parameters are dependent on the applied stress and cavity conditions. Parameters that clearly affect PD activity can be readily identifed. These parameters include; the effective charge decay time constant, the cavity surface conductivity, the initial electron generation rate, the inception field, the extinction field and the temperature decay time constant in the cavity. The infuences of surface charge decay through conduction along the cavity wall and temperature and pressure change in the cavity on PD activity have also been studied

Published
2011

14. Modelling spatiotemporal impact of flash floods on power distribution system and dynamic service restoration with renewable generators considering interdependent critical loads.

Author

Afzal, Suhail, Mokhlis, Hazlie, Illias, Hazlee Azil, Bajwa, Abdullah Akram, Mekhilef, Saad, Mubin, Marizan, Muhammad, Munir Azam, and Shareef, Hussain

Subjects
WINDSTORMS, DYNAMICAL systems, INFRASTRUCTURE (Economics), FLOODS, DYNAMIC loads, CITIES & towns
Abstract

In recent decades, flash floods have become more common because of climate change and are considered a substantial risk for many cities worldwide. This catastrophic natural hazard presents a significant threat to critical infrastructure in urban areas, particularly the power distribution system. As modern societies are much more dependent on electrical energy these days, it is essential and imperative to make existing distribution systems resilient against flash flooding. Although researchers in this area have proposed various algorithms to impart resilience to a distribution system, however, the focus in these works is on wind‐related events such as hurricanes, cyclones, and windstorms. Therefore, here, the spatiotemporal effects of a flash flood on the distribution system are modelled using a grid‐based hydrodynamic model. The evolving line faults are then included in the proposed resilience‐oriented time horizon‐based service restoration model that also considers dynamic load demand, heavy uncertainties related to renewable generation, and interdependence among critical loads. Finally, the resilience of the distribution system's response is assessed using an operational resilience metric. The efficacy of the proposed framework is evaluated on IEEE 33‐bus and 69‐bus systems and the results show that the model provides an efficient restoration solution despite increased complexity caused by varying conditions. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

22. Ocean Wave Energy Harvesting via Scotch Yoke-based Rotational Generation

Author

Hossain, Md Zahir, Illias, Hazlee Azil, Hossain, Md Zahir, and Illias, Hazlee Azil

Abstract

Harvesting energy from ocean wave is a promising renewable energy source due to its high efficiency, carbon-free, it is not affected by depletion of fossil fuels, and it is replenished constantly. The existing generators used in wave energy harvesting such as linear generator and slider crank mechanism have some limitations, which include lower efficiency and technologically challenging. Thus, in this work, wave energy harvesting technique using rotational generator based on scotch yoke mechanism is proposed. A typical slider crank to convert linear motion into rotational motion is replaced by scotch yoke mechanism to yield higher efficiency. A comparison of the performance of wave energy system between employing slider crank and scotch yoke is made to evaluate the superiority of the proposed mechanism. From the results obtained in this work, it is found that the output voltage of rotational generator employing scotch yoke mechanism is higher compared to that of rotational generator employing slider crank for the same input power. Thus, rotational generator by employing scotch yoke mechanism can be one of the alternative methods for ocean wave energy harvesting.

Published
2022

32. 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

33. 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

40. 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
Full Text
View/download PDF

46. 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

50. Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions

Author

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

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

Catalog

Books, media, physical & digital resources
See catalog results