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

Research Output

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Multiple Partial Discharge Signal Classification Using Artificial Neural Network Technique in XLPE Power Cable

2023-02-01 , Halim M.I.A. , Razaly N.Z.M. , Mohamad Nur Khairul Hafizi Rohani , Norfadilah Rosle , Auni W.N. , Afifah Shuhada Rosmi , Muhammad Zaid Aihsan , Mohd Aminudin Jamlos , Mas’ud A.A.

According to partial discharge (PD) damage in the electrodes that are not entirely bridging, the presence of PD in the high voltage (HV) power cable might lead to insulation failure. PD defects can damage cross-linked polyethylene (XLPE) cables directly, which is one of the most critical electrical issues in the industry. Poor workmanship during cable jointing, aging, or exposure to the surrounding environment is the most common cause of PD in HV cable systems. As a result, the location of the PD signals that occur cannot be classified without identifying the multiple PD signals present in the cable system. In this study, the artificial neural network (ANN) based feedforward back propagation classification technique is used as a diagnostic tool thru MATLAB software in which the PD signal was approached to determine the accuracy of the location PD signal. In addition, statistical feature extraction was added to compare the accuracy of classification with the standard method. The three-point technique is also an approach used to locate PD signals in a single line 11 kV XLPE underground power cable. The results show that the statistical feature extraction had been successful classify the PD signal location with the accuracy of 80% compared to without statistical feature extraction. The distance between PD signals and the PD source affected the result of the three-point technique which proved that a lower error means a near distance between them.

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Review Study of Image De-Noising on Digital Image Processing and Applications

2023-03-01 , Abdulah C.S.K. , Mohamad Nur Khairul Hafizi Rohani , Baharuddin Ismail , Mohd Annuar Mohd Isa , Afifah Shuhada Rosmi , Wan Azani Wan Mustafa , Ahmad Zaidi Abdullah , Wan Nor Munirah Ariffin , Jamil M.K.M.

This paper reviews several studies of image de-noising on digital image processing and applications. Noisy images contain different noise that exist either due to environment or electronic interferences. Ergo, de-noising is crucial to eliminate the noise that disturb data collecting process. The impact of de-noising on image processing can result for accurate and precise data collected from the image. Additionally, de-noising process required several crucial steps that help to enhance knowledge on digital image and its application. Hence, study and understanding de-noising can improve multiple aspect such as image quality, data sensitivity and specificity, accuracy of the collected data, and increase the percentage of each parameter.

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Hilbert fractal UHF sensor based on partial discharge detection signal for on-line condition monitoring in power transformer

2020-01-01 , Roslizan N.D. , Mohamad Nur Khairul Hafizi Rohani , Afifah Shuhada Rosmi , Baharuddin Ismail , Muzamir Isa , Akashah N.A. , Mukhtaruddin A.

PD detection is an effective method of inspecting insulation defects and identifying potential faults in a power transformer. Electromagnetic waves generated due to PD can be detected by ultrahigh-frequency (UHF) sensor in the frequency band greater than 300 MHz. However, the size and the frequency bandwidth of a UHF sensor for PD detection are the concern for practical installation inside a transformer. High sensitivity and wide bandwidth of sensors are needed in order to detect the PD signal in an early stage. This paper presents an array 4th order Hilbert fractal UHF sensor for PD detection inside a power transformer. This UHF sensor was modeled to capture PD signal in a range of frequencies between 300 MHz to 3 GHz. The sensor is designed by using CST software where the transmission lines combined 2 sensors become 1 output by setting the dimension of 100 x 200 mm for length and width with FR4 epoxy substrate of thickness 1.6 mm. Based on the simulation result, the proposed sensor is obtained a PD signal measurement with a reflection coefficient below-10 dB with VSWR ≤5. The advantages of this sensor have a wide bandwidth, high sensitivity and suitable size for easy installation. Thus, this sensor has been qualified as UHF PD detection in the power transformer.

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A review: Partial discharge sensor applications and classification technique in high voltage cable

2020-01-01 , Auni W.N. , Mohamad Nur Khairul Hafizi Rohani , Roslee N.F. , Afifah Shuhada Rosmi , Kamaro M. , Mohd Aizam T. , Jalil M.A.A.

Partial discharge (PD)can cause a failure at high voltage (HV) equipment. Internal discharge, surface discharge and corona discharge can be identified as PD types which can lead to HV system failure. Power cable is one of the major applications in transmission line and power distribution. Therefore, early detection of PD at power cable is important due to prevent any sign of failure. In this paper reviews on how the PD present in power cable and some methods of PD detection at HV equipment. This review highlight on some application of AE sensor and electrical sensor in power cable. Since the PD signals are hard to differentiate due to noise surrounding during experiment, de-noising techniques are proposed in order to remove unwanted PD signal. Next, three popular techniques like Adaptive Neuro-Fuzzy Inference system (ANFIS), Artificial Neural Network (ANN) and Support Vector Machine (SVM) are review in the section of classification of PD signal. Feature extraction act as input of PD classification also introduced to reduce the size of PD data.

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Partial discharge signal measurement based on stand-alone and hybrid detection technique for power transformer

2020-01-01 , Jalil M.A.A. , Afifah Shuhada Rosmi , Mohamad Nur Khairul Hafizi Rohani , Muzamir Isa , Auni W.N. , Ahmad Zaidi Abdullah , Aizam M.T.

Partial discharge (PD) is a phenomenon that causes failures in high voltage (HV) components due to the degradation of insulation. Before an interruption or fault occurs, early detection of insulation degradation is essential. However, the long-term effect of PD will lead to the failure of the power system. This is important to control and diagnose the health of the HV power equipment such as power transformer. The main issue when measuring PD is the accuracy and sensitivity of the PD detection technique. This paper consists of two parts which are classification of the PD detection technique and hybrid detection technique. In this paper, an overview of the detection technique for power transformer including optical detection, chemical detection, electrical detection, electromagnetic detection, acoustic emission detection and hybrid detection technique is presented. The hybrid detection technique is based on combining two or more stand-alone detection technique. Based on this review, the hybrid detection technique showed that the advantages of performance in terms of sensitivity and accuracy for detecting the PD in power transformer.

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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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Comparison of Image Restoration using Median, Wiener, and Gaussian Filtering Techniques based on Electrical Tree

2021-01-01 , Abdulah C.S.K. , Mohamad Nur Khairul Hafizi Rohani , Baharuddin Ismail , Isa M.A.M. , Afifah Shuhada Rosmi , Wan Azani Wan Mustafa

Electrical treeing lead to a major cause of a breakdown in solid insulation. Thus reduced solid insulation performance by degrading the insulation. Hence, it is important to study the electrical treeing and learn the root cause of the treeing formation. In this paper, the performances of median, wiener, and gaussian filters in restoring noisy images are studied based on electrical tree images. The electrical tree colour images is being transform into grayscale images, noisy images using impulse noise (salt and pepper), and finally motion blur are applied. Even though, there are several number of filters available, this paper focus on median, wiener, gaussian, and combination of the filters. In the end, comparison between these filters is made to study the efficiency using PSNR, SNR, and MSE in graph form.

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A review: Partial discharge detection using acoustic sensor on high voltage transformer

2020-01-07 , Akashah N.A. , Mohamad Nur Khairul Hafizi Rohani , Afifah Shuhada Rosmi , Muzamir Isa , Norfadilah Rosle , Baharuddin Ismail , Wooi Chin Leong

Partial discharge (PD) is an electrical discharge which is one of the most critical breakdown factor that is affecting the electrical equipment. The loss of the power will affect consumers and system operation. High voltage (HV) transformer is one of the equipment's subjected to phenomena PD. In this paper reviews an application of acoustic methods in transformer and piezoelectric sensors application on PD detection in HV transformer. Based on this review, the new design in acoustic sensor is required in order to improve the sensitivity and bandwidth for PD detection at HV transformer. The valuable parameter such as materials, size, and PD frequency range were discussed in this paper and can be used for early stage on designing new acoustic sensor. This detection method given some benefits on preventing the power electrical system from breakdown.

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Electrical Tree Image Segmentation Using Hybrid Multi Scale Line Tracking Algorithm

2023-01-01 , Mohd Annuar Mohd Isa , Mohamad Nur Khairul Hafizi Rohani , Baharuddin Ismail , Jamil M.K. , Muzamir Isa , Afifah Shuhada Rosmi , Mohd Aminudin Jamlos , Wan Azani Wan Mustafa , Nurulbariah Idris , Mas’ud A.A.

Electrical trees are an aging mechanism most associated with partial discharge (PD) activities in crosslinked polyethylene (XLPE) insulation of high-voltage (HV) cables. Characterization of electrical tree structures gained considerable attention from researchers since a deep understanding of the tree morphology is required to develop new insulation material. Two-dimensional (2D) optical microscopy is primarily used to examine tree structures and propagation shapes with image segmentation methods. However, since electrical trees can emerge in different shapes such as bush-type or branch-type, treeing images are complicated to segment due to manifestation of convoluted tree branches, leading to a high misclassification rate during segmentation. Therefore, this study proposed a new method for segmenting 2D electrical tree images based on the multi-scale line tracking algorithm (MSLTA) by integrating batch processing method. The proposed method, h-MSLTA aims to provide accurate segmentation of electrical tree images obtained over a period of tree propagation observation under optical microscopy. The initial phase involves XLPE sample preparation and treeing image acquisition under real-time microscopy observation. The treeing images are then sampled and binarized in pre-processing. In the next phase, segmentation of tree structures is performed using the h-MSLTA by utilizing batch processing in multiple instances of treeing duration. Finally, the comparative investigation has been conducted using standard performance assessment metrics, including accuracy, sensitivity, specificity, Dice coefficient and Matthew’s correlation coefficient (MCC). Based on segmentation performance evaluation against several established segmentation methods, h-MSLTA achieved better results of 95.43% accuracy, 97.28% specificity, 69.43% sensitivity rate with 23.38% and 24.16% average improvement in Dice coefficient and MCC score respectively over the original algorithm. In addition, h-MSLTA produced accurate measurement results of global tree parameters of length and width in comparison with the ground truth image. These results indicated that the proposed method had a solid performance in terms of segmenting electrical tree branches in 2D treeing images compared to other established techniques.

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Modelling of piezoelectric sensor based on ZnO material for partial discharge detection on power transformer

2020-01-01 , Akashah N.A. , Mohamad Nur Khairul Hafizi Rohani , Afifah Shuhada Rosmi , Muzamir Isa , Baharuddin Ismail , Kamarol M. , Roslizan N.D. , Khairul Nadiah Khalid

Detection of partial discharge (PD) in early stages able to reduce the risk of decommissioning of high voltage (HV) equipment. However, the conventional method for PD detection are not suitable for on-site measurement due to electrical disturbance. One of the method in detecting PD signal is piezoelectric based acoustic emission (AE) sensors. In this project, an AE sensor is modelled to obtain a PD signal in the range of 10 – 300 kHz occurred in HV transformer and been found out by simulation and analytical approach. Two models of a piezoelectric sensor with different types of cantilever and different dimension variation starting from 4 mm to 15 mm are designed in the Finite element Method (FEM) in order to investigate the resonant frequency which is matched to the range of AE detection. Zinc oxide (ZnO) as a piezoelectric material is proposed in this project due to its high piezoelectric coupling and environmentally friendly compared to the others material which is harmful. Based on the simulation result, ZnO piezoelectric sensor with the length of 5 mm and thickness of 0.451 mm generates 0.0537 mV electrical potentials under the resonant frequency of 155.30 kHz which is in the range of AE detection technique.

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