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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Optimal Allocation and Sizing of Multi DG Units including Different Load Model Using Evolutionary Programming

2021-06-11 , Mohd Fayzul Mohammed , Siti Rafidah Abdul Rahim , Azralmukmin Azmi , Wan Zulmajdi Wan Zanudin , Muhamad Hatta Hussain , Nurul Huda H. , Aliman O.

This paper presents the optimal allocation and sizing of multi distributed generation (DG) units including different load models using evolutionary programming (EP) in solving power system optimization problem. This paper also studies on the effect of multi DG placement in different load model. To optimize the power distribution system, multi DG units were used to reduce losses power distribution system. By using EP, the optimal allocation and sizing of multi-DG was determined in order to obtain maximum benefits from its installation. The propose technique was tested into IEEE 69-bus distribution system. The result shows the placement of DG can reduce power loss 89% to 98%. The placement of multi-DG unit has better performance compare to single DG.

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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.

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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.

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