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
    Integration of Multiple Distributed Generation Sources in Radial Distribution System Using a Hybrid Evolutionary Programming-Firefly Algorithm
    (Universiti Malaysia Perlis, 2024-02-29)
    Nik Hasmadi Nik Hassan
    ;
    Siti Rafidah Abdul Rahim
    ;
    Muhamad Hatta Hussain
    ;
    Syahrul Ashikin Azmi
    ;
    Azralmukmin Azmi
    ;
    Ismail Musirin
    ;
    Sazwan Ishak
    This paper presents an approach for the optimal integration of multiple distributed generation (DG) sources in a radial distribution system. The integration of DG sources poses various challenges such as can lead to higher power losses caused by reverse power flow, voltage exceeding secure limits, voltage stability, power quality, and economic operation. To address these challenges, a hybrid algorithm is proposed which combines the benefits of both Evolutionary Programming and Firefly Algorithm. The proposed hybrid Evolutionary - Firefly Algorithm is employed for the determination of the optimal size of the DG sources. The objective of the proposed algorithm is to minimize the total system power losses and improve the voltage profile. The algorithm considers various constraints including the DG capacity limits and voltage limits. A comprehensive case study is conducted on a radial distribution system to demonstrate the effectiveness of the proposed approach. The simulation results show that the hybrid algorithm can find the optimal size and location of DG sources while achieving the desired system performance. The integration of multiple DG sources leads to a significant reduction in power losses and improved voltage profile. Furthermore, the proposed approach provides a flexible framework for the optimal integration of DG sources in radial distribution systems, allowing for the accommodation of different types and capacities of DG sources. The proposed technique is tested on the IEEE Reliability Test systems, specifically the IEEE 69-bus. The combination of DG at bus 61 and bus 27 yields a loss reduction index of 94%.
  • Publication
    A comparative study on DG placement using marine predator and Osprey algorithms to enhance loss reduction index in the distribution system
    (Iran University of Science and Technology, 2025-06)
    Siti Rafidah Abdul Rahim
    ;
    Azralmukmin Azmi
    ;
    Syahrul Ashikin Azmi
    ;
    Muhamad Hatta Hussain
    ;
    Syazwan Ahmad Sabri
    ;
    Ismail Musirin
    The Marine Predator Algorithm (MPA) and Osprey Optimization Algorithm (OOA) are nature-inspired metaheuristic techniques used for optimizing the location and sizing of distributed generation (DG) in power distribution systems. MPA simulates marine predators' foraging strategies through Lévy and Brownian movements, while OOA models the hunting and survival tactics of ospreys, known for their remarkable fishing skills. Effective placement and sizing of DG units are crucial for minimizing network losses and ensuring cost efficiency. Improper configurations can lead to overcompensation or undercompensation in the network, increasing operational costs. Different DG technologies, such as photovoltaic (PV), wind, microturbines, and generators, vary significantly in cost and performance, highlighting the importance of selecting the right models and designs. This study compares MPA and OOA in optimizing the placement of multiple DGs with two types of power injection which are active and reactive power. Simulations on the IEEE 69-bus reliability test system, conducted using MATLAB, demonstrated MPA’s superiority, achieving a 69% reduction in active power losses compared to OOA’s 61%, highlighting its potential for more efficient DG placement in power distribution systems. The proposed approach incorporates a DG model encompassing multiple technologies to ensure economic feasibility and improve overall system performance.
  • 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
    Particle Swarm Optimization for Directional Overcurrent Relay Coordination with Distributed Generation
    (Universiti Malaysia Perlis, 2024-02)
    A. S. M. Adnan
    ;
    Muhamad Hatta Hussain
    ;
    Siti Rafidah Abdul Rahim
    ;
    Azralmukmin Azmi
    ;
    Musirin
    ;
    J. A. Radziyan
    The Directional Overcurrent Relays (DOCRs) Coordination with Distributed Generation (DG) optimization problem is addressed in this study using the optimization method Particle Swarm Optimization (PSO). Changes in fault current, bus voltages, power flow, and reliability may result from DG integration. Thus, it might have an impact on the current protection coordination system. The formulation is built on a Mixed Integer Non-Linear Programming (MINLP) problem to address this DOCR issue. MATLAB was used to validate the technique on the IEEE-14 bus system, and Electrical Test Transient Analyzer Programming (ETAP) version 2021 software was used to model the test system. According to the simulation results, the suggested PSO with DG for Case 2 has reduced power loss by 6.24% and relay operating time by 46.79% when compared to PSO without the presence of DG.
      7  1