Siti Rafidah Abdul Rahim
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Preferred name
Siti Rafidah Abdul Rahim
Official Name
Siti Rafidah, Abdul Rahim
Alternative Name
Abdul Rahim, Siti Rafidah
Rahim, S. R.
Rahim, S.R.A
Abdul Rahim, S.R.
Rahim, S.R. Abd
Main Affiliation
Scopus Author ID
11639107900
Researcher ID
E-5412-2011

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A hybrid multi-objective Evolutionary Programming-Firefly Algorithm for different type of Distributed Generation in distribution system

2022-12-01 , Noor Najwa Husnaini Mohammad Husni , Siti Rafidah Abdul Rahim , Mohd Rafi Adzman , Hussain M.H. , Musirin I. , Syahrul Ashikin Azmi

With the rise in electricity demand, various additional sources of generation, known as Distributed Generation (DG), have been introduced to boost the performance of power systems. A hybrid multi-objective Evolutionary Programming-Firefly Algorithm (MOEPFA) technique is presented in this study for solving multi-objective power system problems which are minimizing total active and reactive power losses and improving voltage profile while considering the cost of energy losses. This MOEPFA is developed by embedding Firefly Algorithm (FA) features into the conventional EP method. The analysis in this study considered DG with 4 different scenarios. Scenario 1 is the base case or without DG, scenario 2 is for DG with injected active power, scenario 3 is for DG injected with reactive power only and scenario 4 is for DG injected with both active and reactive power. The IEEE 69-bus test system is applied to validate the suggested technique.

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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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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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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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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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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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Comparative Study of Power System Security Assessment using Deterministic and Probabilistic Methods

2023-01-01 , Aminudin N. , Musirin I. , Salimin R.H. , Yusoh M.A.T.M. , Siti Rafidah Abdul Rahim , Yusof Y.

Escalating electricity demand has forced the power system to operate very close to its security margin. Any unpredictable occurrence of a contingency would exacerbate the condition and threaten power system security. Thus, it is of utmost importance for the system operator to evaluate the actual system health accurately to avoid voltage collapse incidents and to evade overly conservative protection. This paper presents a risk-based security assessment (RBSA) in power system operation that quantifies the degree of risk faced by the system in its proximity to voltage stability violations due to transmission line outages that occur in the system. The risk value is calculated by considering the closeness of the system condition to the point of instability, which is also regarded as severity, as well as the likelihood of the contingency to occur. In the research, the performance of RBSA is compared with the traditional deterministic method in assessing power system conditions. The IEEE 30 bus system is engaged as the test system, and the simulation is done using MATLAB software.

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Multi-DGPV Planning Using Artificial Intelligence

2023-02-13 , Abdullah A. , Musirin I. , Othman M.M. , Siti Rafidah Abdul Rahim , Sentilkumar A.V.

This article investigates the impact of multi-Distributed Generation Photovoltaic (DGPV) installation and their degree of penetration on controlling power loss in the radial distribution system. The Integrated Immune Moth Flame Evolution Programming (IIMFEP), a unique hybrid optimization technique, was utilized to identify the ideal DGPV size and location for base case conditions and under load variations. The IIMFEP approach is compared against Evolutionary Programming (EP), Artificial Immune System (AIS), and Moth Flame Optimization (MFO) and validated using the IEEE 118-Bus Radial Distribution Systems (RDS). Incorporating multi-DGPV into a system reduces the total real and reactive power loss while simultaneously increasing the minimum voltage and decreasing the total voltage deviation. In every instance examined in this study, the IIMFEP method yields optimal solutions superior to those generated by the other three methods. As the number of DGPV units increased to nine, the percentage of power loss reduction became the highest among all DG units examined, and DG penetration reached 94.26 percent. This research provides the power system operator with comprehensive findings demonstrating the impact of installing multi-DGPV in distribution networks on system loss.

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