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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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.

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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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Particle Swarm Optimization for Directional Overcurrent Relay Coordination with Distributed Generation

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.

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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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Integration of Multiple Distributed Generation Sources in Radial Distribution System Using a Hybrid Evolutionary Programming-Firefly Algorithm

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%.

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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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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.

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A comparative study on DG placement using marine predator and Osprey algorithms to enhance loss reduction index in the distribution system

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.

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An automatic irrigation system for plants using fuzzy logic controller considering volumetric water content

2020-01-07 , Rahim S. , Muhamad Hatta Hussain , Siti Rafidah Abdul Rahim , Hashim N. , Halim H.A.

An automatic irrigation system have an important role in today agriculture sector. Currently, automatic irrigation systems which can irrigate and supply plants with desired level and amount of water required for normal plant growth are not available. Furthermore, the classical method using on-off controller is inefficient due to results in loss of energy and productivity. Thus, an efficient automatic irrigation system with fuzzy logic controller is proposed to estimate the amount of water level using the irrigation model, soil type and type of plant. This paper presents a solution for irrigation system for plants using fuzzy logic controller using MATLAB Simulink. To indicate the effectiveness of the proposed method, on-off controller method is also applied for validation purpose.

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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.

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