Norfadilah Rosle
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Preferred name
Norfadilah Rosle
Official Name
Norfadilah, Rosle
Alternative Name
Rosle, N.
Rosle, N. F.
Rosle, Norfadilah
Main Affiliation
Scopus Author ID
57208407506
Researcher ID
DRD-5901-2022

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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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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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Simulation study on photovoltaic panel temperature under different solar radiation using computational fluid dynamic method

2020-01-07 , Leow W.Z. , Mohd Irwan Yusoff , Safwati Ibrahim , Muhammad Irwanto Misrun , Amelia A.R. , Syafiqah Z. , Muhammad Izuan Fahmi Romli , Norfadilah Rosle

The electrical production is the primary performance of any solar photovoltaic (PV) system. The PV panel operating temperature is inversely proportional to the electrical production of the PV panel. The operating temperature of PV panel is influenced by solar radiation absorbed and the ambient temperature. In the present work, Computational Fluid Dynamics (CFD) method is used to investigate a three-dimensional (3-D) model of a PV panel. It is also essential to estimate the thermal behaviour of the PV panel under various environmental conditions. The primary purpose of this current work is to analyse temperature distribution from the PV panel under given operating conditions. The model geometry is built by using CATIA design software. ANSYS software was simulated the different intensity of solar radiation that applied to the PV panel in order to observe the temperature distribution on each layers of the PV panel. The ambient temperature of the simulation is fixed 35C according to the maximum ambient temperature captured in Malaysia. The simulation results show that an increase in solar radiation intensity along with the PV panel operating temperature increase.

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Improvement of electrical power system dynamic stability using Riccati Matrix method

2015 , Norfadilah Rosle

Low frequency oscillations appear when a small disturbance occurs in an interconnected multi-machine power system and becomes one of the major subjects concerning in power system studies. Due to small disturbances, power systems experience these poorly damped low-frequency oscillations. In the dynamic electrical power system stability, the positive damping oscillation is very important. By using the optimum gains that obtained from Riccati matrix method, these oscillations can be well damped and hence the system stability is enhanced. Riccati matrix method is used to enhance better system stability in damping oscillation power system. This research presents a design of a single machine to an infinite bus (SMIB) with additional optimum gain from Riccati matrix method to enhance the dynamic stability of power systems by improving the damping of the low frequency oscillations. Eigenvalues analysis and time domain analysis are applied to the overall system. MATLAB Simulink is used to design a Riccati matrix method to enhance the damping characteristic of power system to improve its stability. The dynamic simulations results are presented to show the effectiveness and robustness of the designed Riccati matrix method. Analysis and simulation have proved the effectiveness of a Riccati matrix method in order to improve the electrical power system dynamic stability.

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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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Load shedding analysis on microgrid during island mode

2020 , Nur Najihah Abu Bakar , A’lia Najwa Muhamad Azmi , Norfadilah Rosle , Siti Sufiah Abd Wahid , Mohd Sufian Ramli

This paper evaluates implementation load shedding strategy in island mode of microgrid(MG). Microgrid normally operates in interconnected mode either with the medium voltage(MV) and low voltage(LV) network. Microgrid can function both in grid and island mode connected. As electricity demand increases, microgrid deployment becomes an attractive option to meet energy demands. Microgrid during utility grid failure, however, suffers from crucial stability problems come from many aspects. Load Shedding Strategy (LSS) is one of the method used to sustain operation of power system in stable state. The main objective in this paper is to analyze the implementation of Load Shedding Strategy (LSS) on two different cases. The simulation model developed from a mix of generator, photovoltaic cell of source and the lumped load. ETAP software was used in analysing the result.

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Fault detection and classification in three phase series compensated transmission line using ANN

2020 , Norfadilah Rosle , Noor Fazliana Fadzail , M I A Halim , Mohamad Nur Khairul Hafizi Rohani , Muhammad Izuan Fahmi Romli , Leow Wai Zhe , Nur Najihah Abu Bakar

Series compensation consists of capacitors in series is used in the transmission lines as a tool to improve the performance after disturbed by a fault. Transmission line needs a protection scheme to protect the lines from faults due to natural disturbances, short circuit and open circuit faults. The fault can happen in any location of transmission line and it is important to know which location has been affected. So that, the fault can be eliminated and can maintain the optimum performance. Therefore, in this paper Artificial Neural Network (ANN) is used to detect and classified the fault happen in single line to ground fault and three phase to ground fault. Two different tests of each types of fault have been tested in order to prove the effectiveness of ANN to detect the fault location by using different length and fault resistance. The simulation has been accomplished in MATLAB with ANN fitting tool which build and train the network before evaluated its performance using regression analysis. The analysis shows that the ANN can accurately detect the different types of faults and classified it into the respective category even the random vectors are put on the system are used.

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