Noor Fazliana Fadzail
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Preferred name
Noor Fazliana Fadzail
Official Name
Noor Fazliana, Fadzail
Alternative Name
Fadzail, Noor Fazliana Bt
Fadzail, N. F.
Fadzail, Noor Fazliana
Binti Fadzail, Noor Fazliana
Main Affiliation
Scopus Author ID
57193318102
Researcher ID
CPB-1523-2022

Research Output
Now showing 1 - 6 of 6
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Publication

Stator winding fault detection of induction generator based wind turbine using ANN

2020-07 , Noor Fazliana Fadzail , Samila Mat Zali , M. A. Khairudin , Noor Hasnizam Hanafi

This paper presents a stator winding faults detection in induction generator based wind turbines by using artificial neural network (ANN). Stator winding faults of induction generators are the most common fault found in wind turbines. This fault may lead to wind turbine failure. Therefore, fault detection in induction generator based wind turbines is vital to increase the reliability of wind turbines. In this project, the mathematical model of induction generator based wind turbine was developed in MATLAB Simulink. The value of impedance in the induction generators was changed to simulate the inter-turn short circuit and open circuit faults. The simulated responses of the induction generators were used as inputs in the ANN model for fault detection procedures. A set of data was taken under different conditions, i.e. normal condition, inter-turn short circuit and open circuit faults as inputs for the ANN model. The target outputs of the ANN model were set as ‘0’ or ‘1’, based on the fault conditions. Results obtained showed that the ANN model can detect different types of faults based on the output values of the ANN model. In conclusion, the stator winding faults detection procedure for induction generator based wind turbines by using ANN was successfully developed.

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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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Analysis of fault detection and classification in photovoltaic arrays using neural network-based methods

2025-06 , Arizadayana Zahalan , Samila Mat Zali , Ernie Che Mid , Noor Fazliana Fadzail

Photovoltaic (PV) systems are vital in the global renewable energy landscape because of their capability to harness solar energy efficiently. Ensuring the continuous and efficient operation of PV systems is crucial in maximizing their energy contribution. However, these systems' reliability and safety remain critical because they are prone to various faults, mainly when operating in harsh environmental conditions. This study addresses these issues by exploring fault detection and classification in PV arrays using neural network (NN) -based techniques. A PV array model, consisting of 3x6 PV modules, was simulated using MATLAB Simulink to replicate real-world conditions and analyse various fault scenarios. An open circuit, a short circuit, and a degrading fault are the three types of faults considered in this study. The NN was trained on a dataset generated from the MATLAB Simulink model, encompassing normal operating and fault conditions. This training enables the network to learn the distinctive patterns associated with each fault type, enhancing its detection accuracy and classification capabilities. Simulation results demonstrate that the NN-based approach effectively identifies and classifies the three types of faults.

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Publication

Dynamic model of distribution network cell using artificial intelligence approach

2013 , Noor Fazliana Fadzail

The aim of this project is to develop a dynamic model of distribution network cell (DNC) using artificial intelligence approach. The increasing number of distributed generation (DG) technology has lead to difficulty in modeling the DNC model. The simple load modeling is no longer reliable in presenting the DNC model. In this project,the equivalent dynamic model of DNC consists of the converter-connected generator and the composite load model. The model was developed in the form of seven order state-space model. This model was adopted from Samila Mat Zali in 2012. The parameter estimation of the model was developed using fuzzy system. The parameter value was updated through adaptive neuro-fuzzy inference system (ANFIS). The active and reactive power responses from the fuzzy model were compared with the response from the full DNC model at various types of disturbances. The response of full DNC model was obtained from the UK 11 kV distribution network model. The model was built in DigSILENT PowerFactory software. The full DNC model was also adopted from Samila Mat Zali in 2012. The performance of the fuzzy model was validated by calculating the value of root means square error (RMSE) and the best fit value. Later, the performance of the fuzzy model was also compared with the system identification model by Samila Mat Zali in 2012. The results obtained shown that the fuzzy model was more simple as only a few parameters involved in developing the equivalent model. This simplicity was reflected in the low computational time. The efficiency was also good based on the low RMSE value and high best fit value. In conclusion, the equivalent dynamic model of DNC based on fuzzy system approach was successfully developed.

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Determination of soft starter firing angle performance to mitigate motor high inrush current using current limitation method

2020-03-20 , Mohamad Shukor Abdul Rahim , Azizan, Muhammad Mokhzaini , Mohd Irwan Yusoff , Mohd Hafizuddin Mat , Nur Irwany Ahmad , Noor Fazliana Fadzail , Md Esa, Suhaireza Binti

Inrush current in the simplest form can also be determined as current drawn by an induction motor during startup period. This starting current will shoot up about 5 to 7 times the rated current. However, this high current usually occur in the starting period only. To overcome this, several techniques can be implemented to reduce the high current. The configuration of soft starter just involving some power semiconductor device act as switches that control the current flow from power source to the motor. The switches is in form of thyristor and are connected back-to-back because the system conduct in AC system. The current output can be controlled by varying the firing angle. This changing of firing angle will be managed by a firing angle control circuit. This soft starter was connected between power source and motor. The thyristors that built in soft starter act like a gate to control the voltage applied to the motor. The firing angle for current limitation soft starter was changed to several angle and what can be concluded that the high current succeed to mitigate with increasing the firing angle. The current drawn for this type of starter is steadily constant. The lower current during starting took longer time for motor to reach its rated speed. This type of starter successfully reduces inrush current about 42 percent. Finally what can be concluded is that the soft starter was proven to mitigate inrush current. Type of soft starter that going to implement is depending on the application of motor. When the application need to control the torque is more suitable to use current limitation soft starter because the current is steadily control.

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Double sigmoid activation function for fault detection in wind turbine generator using artificial neural network

2025-06 , Noor Fazliana Fadzail , Samila Mat Zali , Ernie Che Mid

The activation function has gained popularity in the research community since it is the most crucial component of the artificial neural network (ANN) algorithm. However, the existing activation function is unable to accurately capture the value of several parameters that are affected by the fault, especially in wind turbines (WT). Therefore, a new activation function is suggested in this paper, which is called the double sigmoid activation function to capture the value of certain parameters that are affected by the fault. The fault detection in WT with a doubly fed induction generator (DFIG) is the basis for the ANN algorithm model that is presented in this study. The ANN model was developed in different activation functions, namely linear and double sigmoid activation functions to evaluate the effectiveness of the proposed activation function. The findings indicate that the model with a double sigmoid activation function has greater accuracy than the model with a linear activation function. Moreover, the double sigmoid activation function provides an accuracy of more than 82% in the ANN algorithm. In conclusion, the simulated response demonstrates that the proposed double sigmoid activation function in the ANN model can effectively be applied in fault detection for DFIG based WT model.

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