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

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  • Publication
    Load shedding analysis on microgrid during island mode
    (IOP Publishing, 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.
  • Publication
    Fault detection and classification in three phase series compensated transmission line using ANN
    (IOP Publishing, 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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