UniMAP RIS :: Browsing Journal Articles by Title

Publication

2D LiDAR based reinforcement learning for Multi-Target path planning in unknown environment

( 2023)

Nasr Abdalmanan

;

Kamarulzaman Kamarudin

;

Muhammad Aizat Abu Bakar

;

Mohd Hafiz Fazalul Rahiman

;

Ammar Zakaria

;

Syed Muhammad Mamduh Syed Zakaria

;

Latifah Munirah Kamarudin

Global path planning techniques have been widely employed in solving path planning problems, however they have been found to be unsuitable for unknown environments. Contrarily, the traditional Q-learning method, which is a common reinforcement learning approach for local path planning, is unable to complete the task for multiple targets. To address these limitations, this paper proposes a modified Q-learning method, called Vector Field Histogram based Q-learning (VFH-QL) utilized the VFH information in state space representation and reward function, based on a 2D LiDAR sensor. We compared the performance of our proposed method with the classical Q-learning method (CQL) through training experiments that were conducted in a simulated environment with a size of 400 square pixels, representing a 20-meter square map. The environment contained static obstacles and a single mobile robot. Two experiments were conducted: experiment A involved path planning for a single target, while experiment B involved path planning for multiple targets. The results of experiment A showed that VFH-QL method had 87.06% less training time and 99.98% better obstacle avoidance compared to CQL. In experiment B, VFH-QL method was found to have an average training time that was 95.69% less than that of the CQL method and 83.99% better path quality. The VFH-QL method was then evaluated using a benchmark dataset. The results indicated that the VFH-QL exhibited superior path quality, with efficiency of 94.89% and improvements of 96.91% and 96.69% over CQL and SARSA in the task of path planning for multiple targets in unknown environments.

4 41

Publication

A biomimetic sensor for the classification of honeys of different floral origin and the detection of adulteration

( 2011-08)

Ammar Zakaria

;

Ali Yeon Md Shakaff

;

Masnan, Maz Jamilah

;

Norazian Subari

;

Nazifah Ahmad Fikri

;

Abdul Hamid Adom

;

Mohd Noor Ahmad

;

Mahmad Nor Jaafar

;

Latifah Munirah Kamarudin

;

Abdul Hallis Abdul Aziz

;

Abu Hassan Abdullah

;

Supri A. Ghani

The major compounds in honey are carbohydrates such as monosaccharides and disaccharides. The same compounds are found in cane-sugar concentrates. Unfortunately when sugar concentrate is added to honey, laboratory assessments are found to be ineffective in detecting this adulteration. Unlike tracing heavy metals in honey, sugar adulterated honey is much trickier and harder to detect, and traditionally it has been very challenging to come up with a suitable method to prove the presence of adulterants in honey products. This paper proposes a combination of array sensing and multi-modality sensor fusion that can effectively discriminate the samples not only based on the compounds present in the sample but also mimic the way humans perceive flavours and aromas. Conversely, analytical instruments are based on chemical separations which may alter the properties of the volatiles or flavours of a particular honey. The present work is focused on classifying 18 samples of different honeys, sugar syrups and adulterated samples using data fusion of electronic nose (e-nose) and electronic tongue (e-tongue) measurements. Each group of samples was evaluated separately by the e-nose and e-tongue. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to separately discriminate monofloral honey from sugar syrup, and polyfloral honey from sugar and adulterated samples using the e-nose and e-tongue. The e-nose was observed to give better separation compared to e-tongue assessment, particularly when LDA was applied. However, when all samples were combined in one classification analysis, neither PCA nor LDA were able to discriminate between honeys of different floral origins, sugar syrup and adulterated samples. By applying a sensor fusion technique, the classification for the 18 different samples was improved. Significant improvement was observed using PCA, while LDA not only improved the discrimination but also gave better classification. An improvement in performance was also observed using a Probabilistic Neural Network classifier when the e-nose and e-tongue data were fused.

1 95

Publication

A comparative study on DG placement using marine predator and Osprey algorithms to enhance loss reduction index in the distribution system

(Iran University of Science and Technology, 2025-06)

Siti Rafidah Abdul Rahim

;

Azralmukmin Azmi

;

Syahrul Ashikin Azmi

;

Muhamad Hatta Hussain

;

Syazwan Ahmad Sabri

;

Ismail Musirin

The Marine Predator Algorithm (MPA) and Osprey Optimization Algorithm (OOA) are nature-inspired metaheuristic techniques used for optimizing the location and sizing of distributed generation (DG) in power distribution systems. MPA simulates marine predators' foraging strategies through Lévy and Brownian movements, while OOA models the hunting and survival tactics of ospreys, known for their remarkable fishing skills. Effective placement and sizing of DG units are crucial for minimizing network losses and ensuring cost efficiency. Improper configurations can lead to overcompensation or undercompensation in the network, increasing operational costs. Different DG technologies, such as photovoltaic (PV), wind, microturbines, and generators, vary significantly in cost and performance, highlighting the importance of selecting the right models and designs. This study compares MPA and OOA in optimizing the placement of multiple DGs with two types of power injection which are active and reactive power. Simulations on the IEEE 69-bus reliability test system, conducted using MATLAB, demonstrated MPA’s superiority, achieving a 69% reduction in active power losses compared to OOA’s 61%, highlighting its potential for more efficient DG placement in power distribution systems. The proposed approach incorporates a DG model encompassing multiple technologies to ensure economic feasibility and improve overall system performance.

Publication

A comparison of double-end partial discharge localization algorithms in power cables

(MDPI, 2023)

Asfarina Abu Bakar

;

Chai Chang Yii

;

Chin Kui Fern

;

Yoong Hou Pin

;

Herwansyah Lago

;

Mohamad Nur Khairul Hafizi Rohani

The double-end partial discharge (PD) measurement method is the most common method for measuring and localizing PD sources in power cables. The sensitivity of the PD sensor, the processing speed of the data acquisition unit, and the method of the PD localization algorithm are the three main keys to ensuring the accuracy of the PD source localization on power cables. A new multi-end PD localization algorithm known as segmented correlation trimmed mean (SCTM) has recently demonstrated excellent accuracy in the localization of PD sources on power cables. The algorithm, however, is only applicable to multi-end PD measurement methods. In this paper, the mathematical equation of the SCTM algorithm is customized to match the double-end PD measurement method. A MATLAB simulation was conducted to assess the performance of the SCTM algorithm in the double-end PD measurement method. The maximum peak detection (MPD) algorithm, segmented correlation (SC), and SCTM algorithm were compared as PD localization algorithms. The SC algorithms have shown that identifying the correlation bond between two cues instead of the peak of the PD signal in the MPD algorithm significantly increases the PD localization accuracy. The results show that the SCTM algorithm outperforms the MPD and SC algorithms in terms of accuracy.

Publication

A comprehensive study: AI literacy as a component of media literacy

(Semarak Ilmu Publishing, 2025-11)

Miharaini Md Ghani

;

Wan Azani Wan Mustafa

;

Durratul Laquesha Shaiful Bakhtiar

;

Moh. Khairudin

The widespread use of AI-based technologies has sparked educational, social, and political interest in AI training. Education systems must prepare individuals for a world with AI. AI literacy is a cognitive and pedagogical difficulty. AI's language and intricacies need redefining literacy. Because these systems are easy to use, more individuals are utilizing them than those with limited conceptualizations (such as an inability to grasp the future relevance of these systems) or competencies (like an inability to comprehend how these systems function). The study investigates the increasing significance of artificial intelligence (AI) literacy within the context of media literacy. As AI technologies permeate various aspects of modern media, the capacity to comprehend and engage critically with these systems has become essential. The paper begins by analyzing the intricate intersection of AI and media, focusing on content creation, dissemination, and consumption. It then emphasizes the importance of AI literacy, which is the ability to comprehend, implement, and evaluate AI technologies critically, similar to traditional media literacy skills. Finally, the paper proposes that AI literacy, as part of media literacy, entails understanding how these systems function and their ethical and societal implications. The paper's conclusion offers a comprehensive framework for incorporating AI literacy into media education curricula, aiming to empower individuals to navigate, evaluate, and responsibly participate in the evolving AI-mediated media landscape.

1 5

Publication

A five-level inverter using SEPIC converter for motor drive application

(AIP Publishing Ltd., 2023)

Muhamad Akmal Aazmi

;

Muhammad Izuan Fahmi Romli

;

A. Z. Iman

The research in inverter is one of the fast-evolving technology in the present era and many researchers start to replace the conventional transformer with different types of circuits. A major problem with the conventional transformers resulting in entire system in expensive range and voluminous. This paper will demonstrate a new technique to replace the conventional transformer concept by using dual DC/DC SEPIC Converter and modified H-bridge inverter to form a five-level inverter with multirange voltage selection which depending on the duty-cycle employed for SEPIC converter. The input DC voltage of the proposed inverter can be stepped-down or stepped-up the five-level output voltage waveform which make it suitable to drive the AC motor drive. A simulation using PowerSim with various duty-cycle values, are used to evaluate the suggested inverter.

11 1

Publication

A heuristic ranking approach on capacity benefit margin determination using pareto-based evolutionary programming technique

( 2015)

Muhammad Murtadha Othman

;

Nurulazmi Abd. Rahman

;

Ismail Musirin

;

Mahmud Fotuhi-Firuzabad

;

Abbas Rajabi-Ghahnavieh

This paper introduces a novel multiobjective approach for capacity benefit margin (CBM) assessment taking into account tie-line reliability of interconnected systems. CBM is the imperative information utilized as a reference by the load-serving entities (LSE) to estimate a certain margin of transfer capability so that a reliable access to generation through interconnected system could be attained. A new Pareto-based evolutionary programming (EP) technique is used to perform a simultaneous determination of CBM for all areas of the interconnected system. The selection of CBM at the Pareto optimal front is proposed to be performed by referring to a heuristic ranking index that takes into account system loss of load expectation (LOLE) in various conditions. Eventually, the power transfer based available transfer capability (ATC) is determined by considering the firm and nonfirm transfers of CBM. A comprehensive set of numerical studies are conducted on the modified IEEE-RTS79 and the performance of the proposed method is numerically investigated in detail. The main advantage of the proposed technique is in terms of flexibility offered to an independent system operator in selecting an appropriate solution of CBM simultaneously for all areas.

4 10

Publication

A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes

( 2019)

Abdul Syafiq Abdull Sukor

;

Ammar Zakaria

;

Norasmadi Abdul Rahim

;

Latifah Munirah Kamarudin

;

Rossi Setchi

;

Hiromitsu Nishizaki

Accurate activity recognition plays a major role in smart homes to provide assistance and support for users, especially elderly and cognitively impaired people. To realize this task, knowledge-driven approaches are one of the emerging research areas that have shown interesting advantages and features. However, several limitations have been associated with these approaches. The produced models are usually incomplete to capture all types of human activities. This resulted in the limited ability to accurately infer users’ activities. This paper presents an alternative approach by combining knowledge-driven with data-driven reasoning to allow activity models to evolve and adapt automatically based on users’ particularities. Firstly, a knowledge-driven reasoning is presented for inferring an initial activity model. The model is then trained using data-driven techniques to produce a dynamic activity model that learns users’ varying action. This approach has been evaluated using a publicly available dataset and the experimental results show the learned activity model yields significantly higher recognition rates compared to the initial activity model.

19 16

Publication

A LabVIEW-based real-time GUI for switched controlled energy harvesting circuit for low voltage application

(Taylor and Francis, 2018)

MD Adnan Shahrukh Khan

;

R. K. Rajkumar

;

C. V. Aravind

;

Y. W. Wong

;

Muhammad Izuan Fahmi Romli

This paper develops a universal Real-Time Graphical User Interface for the first time for low-voltage energy Harvesting. The proposed GUI is built in LabVIEW with NIUSB 621 DAQ that synchronized the data to perform real-time analysis through the use of power electronics. A hybrid Vertical Axis Wind Turbine adapted to a 200 W Permanent Magnet Synchronous Generator is used for incorporating the supercapacitor-based battery charging energy harvesting system. The GUI displays the real-time energy harvesting output readings both graphically and digitally along with wind speed and angular velocity of the turbine. The model is built in such a way so that it could be used as a universal GUI for both wind and solar energy harvesting with minimal adjustment.

7 1

Publication

A recent systematic review of zakat digitalization: efficiency and challenges

(Semarak Ilmu Publishing, 2025-11)

Fathullah Asni

;

Khalilullah Amin Ahmad

;

Muhamad Husni Hasbulah

;

Syahraini Tambak

;

Hanis Hazwani Ahmad

;

Wan Azani Wan Mustafa

The digitalization of zakat, an integral concept in Islamic finance and social welfare, is rapidly gaining momentum worldwide as it has a significant impact, especially on the efficiency of the distribution and collection of zakat. This undoubtedly brings significant benefits to those in need, especially the asnaf. However, the use of technology in zakat also poses substantial challenges, particularly regarding concerns about insecure data security. This study aims to systematically examine and analyse current research on the efficiency and challenges of digital technology in zakat. The study reviews relevant literature indexed in the Scopus and Mendeley databases. As a result, two research themes were identified after a comprehensive analysis: the efficiency of digital technology on zakat and the challenges of digital technology on zakat. The findings of this study can serve as inspiration for other researchers, providing a solid foundation for further exploration and improvement of digital technology on zakat.

Publication

A review of analysis of partial discharge measurements using coupling capacitor in rotating machine

(Iran University of Science and Technology, 2025-06)

Mohamad Nur Khairul Hafizi Rohani

;

Afifah Shuhada Rosmi

;

Ayob Nazmy Nanyan

;

Ahmad Syukri Abd Rahman

;

Nur Dini Athirah Gazata

;

Aiman Ismail Mohamed Jamil

;

Mohd Helmy Halim Abdul Majid

;

Normiza Masturina Samsuddin

Partial discharge (PD) is a critical phenomenon in electrical systems, particularly in high-voltage (HV) equipment like transformers, cables, switchgear, and rotating machines. In rotating machines such as generators and motors, PD is a significant concern as it leads to insulation degradation, potentially resulting in catastrophic failure. Effective and reliable diagnostic techniques are essential for detecting and analyzing PD to ensure the operational safety and longevity of such equipment. Various PD detection methods have been developed, including coupling capacitor (CC), high-frequency current transformer (HFCT), and ultra-high frequency (UHF) techniques, each offering unique advantages in assessing the condition of HV electrical systems. Among these, coupling capacitors have gained significant attention due to their ability to improve the accuracy, sensitivity, and efficiency of PD detection in rotating machines. This study focuses on the advancements in coupling capacitor-based techniques and their critical role in enhancing PD diagnostics for monitoring and maintaining high-voltage rotating machinery.

Publication

A simple duty cycle control technique to minimize torque ripple in open-end winding induction motor

(Institute of Advanced Engineering and Science (IAES), 2023)

Muhammad Zaid Aihsan

;

Auzani Jidin

;

Azrita Alias

;

Tole Sutikno

Modern electric vehicles (EVs) that drive an induction motor (IM) fed by a traction inverter are fast gaining popularity due to their simple configuration and robustness. The direct torque control (DTC) technique is one of the best control methods to drive the IM, especially in open-end winding configurations, as it offers more voltage vectors. However, the existence of hysteresis controllers and improper switching technique causes larger torque ripples that leads to variable switching frequency. The study will be focused on the open-end winding induction motor where the direct current (DC) power is fed from both sides of the stator windings using the dual inverter configuration. To minimize the torque ripples, a simple switching technique using the duty cycle control method is proposed by injecting a high-frequency square wave into the default inverter switching status to form the new pattern of voltage vectors. The effectiveness of the proposed technique is tested through MATLAB/Simulink software and validated experimentally with a lab-scale setup using a dSPACE controller. The findings show that the proposed method reduces torque ripple by over 50% while keeping the DTC's simple structure.

6 1

Publication

A systematic review of phacoemulsification cataract surgery in virtual reality simulators

( 2013-01-27)

Lam Chee Kiang

;

Kenneth Sundaraj

;

Mohd Nazri Sulaiman

The aim of this study was to review the capability of virtual reality simulators in the application of phacoemulsification cataract surgery training. Our review included the scientific publications on cataract surgery simulators that had been developed by different groups of researchers along with commercialized surgical training products, such as EYESI® and PhacoVision®. The review covers the simulation of the main cataract surgery procedures, i.e., corneal incision, capsulorrhexis, phacosculpting, and intraocular lens implantation in various virtual reality surgery simulators. Haptics realism and visual realism of the procedures are the main elements in imitating the actual surgical environment. The involvement of ophthalmology in research on virtual reality since the early 1990s has made a great impact on the development of surgical simulators. Most of the latest cataract surgery training systems are able to offer high fidelity in visual feedback and haptics feedback, but visual realism, such as the rotational movements of an eyeball with response to the force applied by surgical instruments, is still lacking in some of them. The assessment of the surgical tasks carried out on the simulators showed a significant difference in the performance before and after the training.

21 1

Publication

Accelerated thermal aging of Kraft papers impregnated with dielectric liquids

(Institute of Advanced Engineering and Science (IAES), 2023)

Imran Sutan Chairul

;

Norazhar Abu Bakar

;

Sharin Ab Ghani

;

Mohd Shahril Ahmad Khiar

;

Nor Hidayah Rahim

;

Syahrun Nizam Md Arshad @ Hashim

Accelerated thermal aging was conducted on Kraft papers impregnated with mineral insulating oil (MO) and palm insulating oil (PO), and the effect of aging time on the oils and Kraft papers was observed. Each sample consisted of insulating oil, dried Kraft paper, and weighed metal catalysts (copper, iron, zinc, and aluminum) in a bottle. Prior to aging, the bottles were left for 24 h at room temperature for impregnation to take place. The thermal aging experiments were carried out at 130 °C for 250, 500, and 750 h. The properties of the MO and PO (moisture content, acidity, and ultraviolet-visible absorption spectra) and the properties of the Kraft papers (tensile strength and colour) were determined. Results showed that the aged PO had higher moisture content compared with the aged MO. However, the Kraft papers impregnated with PO had better tensile strength after 750 h of aging, which may be attributed to the affinity of PO to moisture. This slows down the hydrolytic degradation mechanism. In terms of colour, the Kraft papers were darker than their original colour as the tensile strength decreased. To conclude, the Kraft paper impregnated with PO had higher tensile strength compared with those impregnated with MO.

Publication

Advanced control strategies for managing circulating currents in islanded microgrid inverters

(Iran University of Science and Technology, 2025-06)

Mohd Hafizuddin Mat

;

Nur Hidayah Ramli

;

Nurul Husna Abd Wahab

;

Norezmi Md Jamal

In islanded microgrids, circulating currents among parallel inverters pose significant challenges to system stability and efficient power distribution. Traditional droop control methods often struggle to manage these currents effectively, leading to inefficiencies and potential system damage. This study introduces an advanced fuzzy-robust droop control strategy that integrates fuzzy logic with robust droop control to address these challenges. By incorporating fuzzy logic, the proposed strategy enhances the adaptability of droop control to varying system conditions, improving the management of circulating currents and ensuring more accurate power sharing among inverters. Comprehensive mathematical modeling and extensive simulation analyses validate the performance of this control strategy. The results show that the fuzzy-robust droop control method significantly outperforms conventional approaches, achieving up to a 70% reduction in circulating currents. This improvement leads to a substantial reduction in power losses and enhances the dynamic response under varying load conditions. Additionally, the strategy improves voltage and frequency regulation, contributing to the overall stability and reliability of the microgrid. The findings provide a robust solution to the longstanding issue of circulating currents, optimizing microgrid operations, and paving the way for more efficient and resilient distributed energy systems. The advanced control strategy presented in this study not only addresses critical challenges but also demonstrates the potential for innovative methodologies to meet the growing demands of future energy infrastructures, where reliability and efficiency are essential.

4

Publication

An alternative approaches to predict flashover voltage on polluted outdoor insulators using artificial intelligence techniques

( 2020-04-01)

Salem, Ali. Ahmed. Ali

;

Rahman, Rahisman Abd

;

Kamarudin M.S.

;

Othman, Nordiana Azlin

;

Jamail, Nor. Akmal. Mohd.

;

Haziah Abdul Hamid

;

Ishak, Mohd. Taufiq

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%.

3 28

Publication

An emotion assessment of stroke patients by using bispectrum features of EEG Signals

( 2020)

Choong Wen Yean

;

Wan Khairunizam Wan Ahmad

;

Wan Azani Wan Mustafa

;

Murugappan Murugappan

;

Yuvaraj Rajamanickam

;

Abdul Hamid Adom

;

Mohammad Iqbal Omar

;

Bong Siao Zheng

;

Ahmad Kadri Junoh

;

Zuradzman Mohamad Razlan

;

Shahriman Abu Bakar

Emotion assessment in stroke patients gives meaningful information to physiotherapists to identify the appropriate method for treatment. This study was aimed to classify the emotions of stroke patients by applying bispectrum features in electroencephalogram (EEG) signals. EEG signals from three groups of subjects, namely stroke patients with left brain damage (LBD), right brain damage (RBD), and normal control (NC), were analyzed for six different emotional states. The estimated bispectrum mapped in the contour plots show the different appearance of nonlinearity in the EEG signals for different emotional states. Bispectrum features were extracted from the alpha (8–13) Hz, beta (13–30) Hz and gamma (30–49) Hz bands, respectively. The k-nearest neighbor (KNN) and probabilistic neural network (PNN) classifiers were used to classify the six emotions in LBD, RBD and NC. The bispectrum features showed statistical significance for all three groups. The beta frequency band was the best performing EEG frequency-sub band for emotion classification. The combination of alpha to gamma bands provides the highest classification accuracy in both KNN and PNN classifiers. Sadness emotion records the highest classification, which was 65.37% in LBD, 71.48% in RBD and 75.56% in NC groups.

26 1

Publication

An evaluation of fuzzy in image enhancement: design and comparison for Penicillium and Aspergillus species

(Semarak Ilmu Publishing, 2025-02)

Haryati Jaafar

;

Farah Nabilah Zabani

;

Nur Rodiatul Raudah Mohamed Radzuan

;

Fatin Norazima Mohamad Ariff

;

Azirah Baharum

The main focus in this study is to enhance and classify the image of a type of filamentous fungi named Penicillium and Aspergillus. For image enhancement, fuzzy-partition gamma adaptive histogram equalization (FpGAHE) is proposed to improve the quality of an image, in particular the low quality of a microscopic image. Two stages have been considered in this technique. In the first stage, a fuzzy partition is developed to handle the inconsistency of the grey level values of the images by introducing a fuzzy set. In the second stage, surrounding neighbourhood is employed to avoid the imbalance data and reduce the drastically changes of brightness values of the image. The performances are evaluated into two parts i.e., image processing and image classification by using the collected microscopic images of fungi species. To evaluate the effectiveness of the proposed technique, the existing techniques, HE, AHE, CLAHE, GC and AGC is compared to the FpGAHE. In image processing, the result attained shows that the proposed technique has a better performance by obtaining the highest value for the PSNR, SSIM and FSIM evaluation for the species of A. terreus in clean condition. Meanwhile, in image classification, five different nearest neighbour classifiers have been tested. The results show the proposed FpGAHE with Improved Fuzzy-Based k Nearest Centroid Neighbour (IFkNCN) classifier perform the best result compare to other nearest neighbour classifier by obtaining the value of 92.59 and 93.95 for the salt and pepper and Gaussian noise corrupted images respectively.

Publication

Analysis of AC-DC converter circuit performance with difference piezoelectric transducer array connection

(Penerbit UTHM, 2020)

Nik Ahmad Zainal Abidin

;

Norkharziana Mohd Nayan

;

Azuwa Ali

;

N. A. Azli

;

N. M. Nordin

This research presents a simulation analysis for the AC-DC converter circuit with a different configurations of the array connection of the piezoelectric sensor. The selection of AC-DC converter circuits is full wave bridge rectifier (FWBR), parallel SSHI (P-SSHI) and parallel voltage multiplier (PVM) with array configuration variation in series (S), parallel (P), series-parallel (SP) and parallel-series (PS). The system optimizes with different load configurations ranging from 10 kΩ to 1 MΩ. The best configuration of AC-DC converter with an appropriate array piezoelectric connection producing the optimum output of harvested power is presented. According to the simulation results, the harvested power produced by using P-SSHI converter connected with 3 parallel piezoelectric transducer array was 85.9% higher than for PVM and 15.88% higher than FWBR.

Publication

Analysis of acoustic sensor placement for PD location in power transformer

(Scientific and Technological Research Council of Türkiye (TÜBİTAK), 2020)

Khairul Nadiah Khalid

;

Rohani, Muhammad Nur Khairul Hafizi

;

Chai Chang YII

;

Baharuddin Ismail

;

Muzamir Isa

;

Wan Nurul Auni Wan Muhammad

Partial discharge (PD) is an abnormal activity that occurs in high-voltage components, such as power cables, switchgear, machines, and power transformers. Such activity needs to be diagnosed for the equipment to last longer as PD could harm the insulation and potentially lead to asset destruction from time to time. Moving one or more externally mounted acoustic sensors to different locations on the transformer tank is commonly used in order to detect and locate PD signal occurring in the power transformer. However, this procedure may lead to less accuracy in PD identification. Therefore, this research paper presents an analysis of acoustic sensor placement based on time of arrival (TOA) technique for PD location in a power transformer. The detection and location can be determined by permanently installing the acoustic sensor to provide valuable data in an early stage of occurrence for online condition PD monitoring. Several methods are available for the detection of PD signal, whereby one of the best choices is via acoustic emission (AE). PD creates an ultrasonic signal used for PD detection. This paper proposes the possible placement of AE sensors to be mounted on the power transformer wall based on ideal and static PD signals. The sensors were placed in order to capture the PD signal without any disturbance signal from inside or outside the tank. The time for the signal for the first approach for each sensor is recorded to estimate the PD location using the TOA technique. A comparison between the least square method (LSM) and Gauss-Jordan elimination (GJE) for the TOA technique was analyzed to differentiate the resulting performance. This research utilized three different PD sources to apply the performance analysis on PD locations, while five cases were proposed to represent the five different placements of four sensors for the analysis. This research ultimately suggests that sensors be placed and randomly mounted on the four sides of the transformer tank, with one sensor allocated to one side. Among all five cases, Case 1 and Case 5 yielded a displacement error (DE) less than others, while between these two cases, Case 5 gave the lowest DE. The findings were recorded based on LSM and GJE methods used to differentiate the resulting performance.

1 6

Journal Articles

Permanent URI for this collection

Browse

Browse

Browsing Journal Articles by Title

Results Per Page

Sort Options