Afifah Shuhada Rosmi
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Afifah Shuhada Rosmi
Official Name
Afifah Shuhada, Rosmi
Alternative Name
Rosmi, Afifah Shuhada
Rosmi, Afifah Shuhada C
Rosmi, A. S.
Rosmi, A. S.C.
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Scopus Author ID
57193830914
Researcher ID
DNN-7471-2022

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Performance evaluation of Edge-based segmentation methods for electrical tree image analysis in high-voltage experiments

2025-06 , Mohd Annuar Mohd Isa , Mohamad Nur Khairul Hafizi Rohani , Afifah Shuhada Rosmi , Baharuddin Ismail , Mohamad Firdaus Azahari , Mohamad Kamarol Jamil , Noor Syazwani Mansor , Abdullahi A. Mas’ud , Firdaus Muhammad-Sukki

This research evaluates the performance of edge-based segmentation methods in analysing two-dimensional (2D) electrical tree images obtained during high-voltage (HV) electrical tree experiments. Non-uniform illumination in 2D optical images poses challenges in accurately extracting and measuring the original treeing image. Edge segmentation emerges as a promising solution to precisely distinguish tree structures from the insulation background within the image. Cross-linked polyethylene (XLPE) samples were subjected to HV stress for real-time propagation observation, followed by extraction and segmentation of treeing images using edge-based operators. The findings emphasize the superiority of the Roberts edge operator in accurately detecting electrical trees, showcasing the highest average accuracy of 97.01% and 99.58% specificity, while also demonstrating relatively high sensitivity. Moreover, the Roberts method provide much precisely measures the propagation length and width than conventional measurement method, closely approximating the actual tree measurements. This research emphasizes the significance of accurate segmentation for investigating electrical tree propagation in XLPE materials and provides recommendations for future research, especially in HV XLPE cable manufacturing.

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Image de-noising based on WMF technique for electrical trees structure in high voltage cable insulation

2025-11 , Siti Khadijah Za'aba , Mohamad Nur Khairul Hafizi Rohani , Baharuddin Ismail , Afifah Shuhada Rosmi

Electrical treeing is a common problem during the pre-breakdown phenomenon in solid insulations due to the damage caused by Partial Discharge (PD) that progresses through stressed insulation via chemical degradation, which resembles the shape of a tree root. This resulted in a decrease in performance through degrading the insulation, which became a serious problem while dealing with electrical equipment. Hence, a deep understanding of electrical tree structure is vital to improving the quality of solid insulations. Ergo, optical microscopy is primarily used to examine tree structures, shapes, and fractal dimensions to reconstruct electrical tree structures for morphological study. However, optical microscopy images are frequently degraded by noise from readout procedures or image data acquisition systems, noise caused by occlusion, illumination, non-uniform intensity, destroying potential tree pixels, and a critical loss of information about the electrical tree structures. Therefore, this research proposed the Wiener Median Fusion (WMF) filter for electrical tree study. The performance of the WMF de-noising technique improves the image quality for the precise portrayal of the electrical tree structure based on thresholding segmentation algorithm analysis in terms of accuracy, sensitivity, and false positive rate. Based on the analysis of the thresholding segmentation algorithm, Otsu's thresholding exhibits the highest result compared to Niblack. The Otsu's overall percentage in terms of accuracy is 80.2934%, the sensitivity is 99.1513%, and the false positive rate is 82.6265%.

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Analysis of array UHF sensor for partial discharge detection in power transformer

2025-08 , Nur Dayini Roslizan , Mohamad Nur Khairul Hafizi Rohani , Afifah Shuhada Rosmi , Baharuddin Ismail , Mohd Aminudin Jamlos , Raimi Dewan

Partial discharges (PD) are small electrical discharges that occur within insulation materials and can lead to equipment failure and safety hazards. The Ultra High Frequency (UHF) sensor consists of a broadband antenna and a UHF receiver, which are designed to detect and analyse the electromagnetic emissions from partial discharges. This paper presents the design and characterization of the 4th order Hilbert fractal UHF sensor for PD detection in power transformer. The sensor is designed to operate in the frequency range of 300 MHz to 3 GHz and is optimized for high gain and directivity. The performance of the sensor is evaluated using simulation and measurement techniques. Simulation results show that the sensor has directivity of 7.9 dBi and reflection coefficient below -10 dB with VSWR ≤ 5.

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Analysis on Multiple Acoustic and Electrical Emission of PD Signal Based on Signal to Noise Ratio (SNR) on Power Cable

2020-12-11 , Mohammad W.N.A.W. , Mohamad Nur Khairul Hafizi Rohani , Norfadilah Rosle , Khairul Nadiah Khalid , Afifah Shuhada Rosmi , Ahmad Zaidi Abdullah , Jamil M.K.M.

Acoustic Emission (AE) and Electrical Emission (EE) partial discharge (PD) monitoring are effective methods in detection of the insulation failure in power cables. However, the unwanted noise from the surrounding environment can influence the effectiveness and accuracy of the PD measurement on the PD signal. Therefore, Discrete Wavelet Transform (DWT) denoising technique is introduced in order to suppress the disrupted noise. In this study, a different type of mother wavelet, level decomposition and its frequency spectrum on multiple AE and EE PD signals were performed via MATLAB software in order to analyze the performance of denoising technique. These PD signals were deal with white noise and Discrete spectral interference (DWT). The better performance of denoising technique is based on evaluating the maximum value of Signal to Noise Ratio (SNR) in order to find the optimum mother wavelet. In this case, the most optimum mother wavelets are rbio3.3 for AE and EE PD signals respectively with the highest value of SNR.

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

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.

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Influence of PD source and AE sensor distance towards arrival time of propagation wave in power transformer

2020-01-07 , Khairul Nadiah Khalid , Mohamad Nur Khairul Hafizi Rohani , Baharuddin Ismail , Muzamir Isa , Afifah Shuhada Rosmi , Wooi Chin Leong , Yii C.C.

Partial discharge (PD) often begins with cracks or gas-filled voids in solid insulation or with gaseous bubbles in liquid insulation. These presences can degrade the quality of insulator. PD detection can identify these cracks at high voltage equipment such as power cables and power transformer at the early stage. One of PD detection methods is acoustic emission (AE) detection. PD produces an ultrasonic signal that can be captured by an AE sensor. The signal captured is then analysed by capturing the time of the receiving signal onto the sensor. The information related to time can be used for allocating the PD for maintenance purpose. This paper shows the influence of the distance between PD source and the AE sensor towards the arrival time of propagation wave in power transformer. In this study, the four placements of sensors were analysed by having three possible PD sources to represent the variety of distances between the PD source and the sensor. The simulated signal is generated by MATLAB and the arrival time is captured using time of arrival (TOA) method. The time captured and the distance between the PD source and arrival time showed that the relationship is proportional to one another.

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