Muhamad Hatta Hussain
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Preferred name
Muhamad Hatta Hussain
Official Name
Muhamad Hatta , Hussain
Alternative Name
Hussain, Muhammad Hatta
Hussain, Muhamad Hatta
Hussain, M. H.
Main Affiliation
Scopus Author ID
36559434400
Researcher ID
DTG-2216-2022

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  • Publication
    Multiple DG planning considering distribution loss and penetration level using EMEFA-ANN method
    ( 2017-07-01)
    Siti Rafidah Abdul Rahim
    ;
    Musirin I.
    ;
    Othman M.
    ;
    Muhamad Hatta Hussain
    This paper presents the implementation of multiple distributed generations planning in distribution system using computational intelligence technique. A pre-developed computational intelligence optimization technique named as Embedded Meta EP-Firefly Algorithm (EMEFA) was utilized to determine distribution loss and penetration level for the purpose of distributed generation (DG) installation. In this study, the Artificial Neural Network (ANN) was used in order to solve the complexity of the multiple DG concepts. EMEFA-ANN was developed to optimize the weight of the ANN to minimize the mean squared error. The proposed method was validated on IEEE 69 Bus distribution system with several load variations scenario. The case study was conducted based on the multiple unit of DG in distribution system by considering the DGs are modeled as type I which is capable of injecting real power. Results obtained from the study could be utilized by the utility and energy commission for loss reduction scheme in distribution system.
  • Publication
    Integrated clustering development using embedded meta evolutionary-firefly algorithm technique for DG planning
    ( 2020-12-01)
    Siti Rafidah Abdul Rahim
    ;
    Musirin I.
    ;
    Othman M.M.
    ;
    Muhamad Hatta Hussain
    ;
    Syahrul Ashikin Azmi
    Recent trend changes have created opportunities to achieve numerous technological innovations including the use of distributed generation (DG) to achieve different advantages. A precise evaluation of energy losses is expanding rapidly when DG is connected to the electricity sector due to developments such as increased competition and real time pricing. Nevertheless, non-optimal DG installation either in the form of DG locations and sizing will lead to possible under-compensation or over-compensation phenomena. The integrated clustering resulted from the pre-developed Embedded Meta Evolutionary Programming-Firefly Algorithm (EMEFA) has been used to ensure the optimum allocation and placement of DG. The study also considers the different types of DG. The aim of the technique is to consider the computational time of the optimization process for DG planning in achieving the minimal total loss. Two test systems have been used as test specimens to achieve the efficacy of the proposed technique. In this study, the techniques proposed were used to establish the DG size and the appropriate place for DG planning. The results for total losses and minimum voltage for the system were recorded from the simulation. The result in this study will be compared with the ranking identification technique to ensure the capability of this technique. The power system planner can adopt the suitable sizes and locations from the obtained result for the planning of utility in term of economic and geographical consideration.
      39  3
  • Publication
    Cost of Energy Losses for Distributed Generation Using Hybrid Evolutionary Programming-Firefly Algorithm
    ( 2021-12-01)
    Noor Najwa Husnaini Mohammad Husni
    ;
    Siti Rafidah Abdul Rahim
    ;
    Mohd Rafi Adzman
    ;
    Muhamad Hatta Hussain
    ;
    Musirin I.
    The cost of energy losses analysis for distributed generation (DG) is presented in this paper using a Hybrid Evolutionary Programming-Firefly Algorithm (EPFA). The proposed method was created to determine the optimal DG sizing in the distribution system while accounting for the system's energy losses. This study presents an investigation into hybrid optimization techniques for DG capabilities and optimal operating strategies in distribution systems. The objectives of this study were to reduce the cost of energy losses while increasing the voltage profile and minimize distribution system losses. In this study, the analysis was done by consider DG type I which is DG-PV. The suggested methodology was tested using the IEEE 69-bus test system, and the simulation was written in the MATLAB programming language. Power system planners can use appropriate location and sizing from the results obtained for utility planning in terms of economic considerations. From the simulation, the result shows the proposed method can identify the suitable sizing of DG while reduce cost of energy losses and total losses in the system.
      31  3
  • Publication
    Optimal distributed generation for loss minimization using Sand Cat Swarm Optimization
    ( 2024-04-15)
    Adnan A.A.S.M.
    ;
    Muhamad Hatta Hussain
    ;
    Siti Rafidah Abdul Rahim
    ;
    Azralmukmin Azmi
    ;
    Musirin I.
    ;
    Radziyan J.A.
    ;
    Mohamad Nur Khairul Hafizi Rohani
    ;
    Nurul Huda H.
    Integration of Distributed Generation (DG) into the transmission system is the current paradigm for creating unique transmission grids. Grid line loss and voltage quality may suffer from unreasonably configured DG. The aim of this paper is to rationally allocate distributed generators (DGs) in the transmission network to reduce power losses and guarantee a safe and reliable power supply to the loads. The works suggests an optimal distributed generation using Sand Cat Swarm Optimization (SCSO) for loss minimization to reduce power loss while enhancing voltage stability. The proposed algorithm was simulated and evaluated using the Matrices Laboratory (MATLAB) script programming language and has been implemented on IEEE 14-bus transmission system. The results exhibit that the SCSO method is able to determine the optimal DG size and reducing total losses by 40.77 percent for DG type 1 as compared with Particle Swarm Optimization (PSO) algorithm, 38.98% at bus 10. It can be revealed that SCSO can be used by power system planners to choose the best sizing and location.
      3  26
  • Publication
    Modified firefly algorithm-artificial neural network based technique for the prediction of time-current characteristic in directional overcurrent relay
    ( 2020-01-01)
    Muhamad Hatta Hussain
    ;
    Musirin I.
    ;
    Siti Rafidah Abdul Rahim
    ;
    Abidin A.F.
    This paper presents an integrated optimal predictor optimization technique termed as Modified Firefly Algorithm-Artificial Neural Network (MFA-ANN) for accurate prediction of Relay Operating Time (ROT). Directional Overcurrent Relays (DOCRs) coordination problem is formulated as Mixed Integer Linear Programming (MILP) problem. The developed techniques have been validated on the IEEE 8-bus systems using MATLAB. The simulation results obtained revealed that the proposed MFA-ANN model has shown the reduction in Root Mean Square Error (RMSE) values as compared with Particle Swarm Optimization-Artificial Neural Network (PSO-ANN) which improved the correlation coefficient of the relay operating time. The proposed MFA-ANN model managed to achieve 0% RMSE value.
      2  27