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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  • 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.
  • Publication
    Optimal Integration of Active and Reactive Power DGs in Distribution Network via a Novel Multi-Objective Intelligent Technique
    ( 2024-01-01)
    Azlina Abdullah
    ;
    Ismail Musirin
    ;
    Muhammad Murthada Othman
    ;
    Siti Rafidah Abdul Rahim
    ;
    Sharifah Azwa Shaaya
    ;
    Senthil Kumar A.V.
    This work introduces a novel approach called the Multi-Objective Integrated Immune Moth Flame Evolutionary Programming (MO-IIMFEP) algorithm. This algorithm aims to determine the optimal sizes and positions for Type III distributed generators (DGs) that generate both active and reactive power. The objectives involve reducing overall losses in the distribution system while adhering to voltage restrictions and taking into account the cost limitations connected with the installation of DG. MO-IIMFEP overcomes the constraints of traditional Evolutionary Programming (EP) and Moth Flame Optimization (MFO), particularly in effectively handling local optima. Fuzzy logic is employed in MO-IIMFEP to determine the best solution to compromise conflicting goals, as obtained from the non-dominated Pareto solutions. The efficacy of MOIIMFEP in identifying optimal solutions for multi-objective problems is demonstrated through comprehensive assessments conducted on the 118-Bus Radial Distribution Systems (RDS), comparing it against MO-EP and MO-MFO. The results underscore the strategic benefits of DG installation in sustaining voltage levels, reducing power losses, and minimizing total operating costs for power suppliers.
  • Publication
    Optimal sizing of a fixed-tilt ground-mounted grid-connected photovoltaic system with bifacial modules using Harris Hawks Optimization
    ( 2024-08-15)
    Nor Syafiqah Syahirah Mohamed
    ;
    Shahril Irwan Sulaiman
    ;
    Siti Rafidah Abdul Rahim
    ;
    Azralmukmin Azmi
    This paper presents an optimal design for ground-mounted grid-connected bifacial PV power plants using a Computational Intelligence (CI)- based Harris Hawks Optimization (HHO) algorithm. This HHO algorithm identifies the best configuration of components and installation parameters for the bifacial PV power plant, aiming to maximize the final yield, minimize the Levelized Cost of Electricity, and boost the Net Present Value. Four variables were optimized: the bifacial PV module model, inverter model, tilt angle, and module elevation. Furthermore, the paper introduces a Harris Hawks Optimization Sizing Algorithm (HHOSA) to address the sizing challenges. The presented HHOSA was purely developed in Matlab R2017b. The usage of PVsyst was only limited to the derivation of irradiation data at different tilt angle of PV array. These data were later used in HHOSA. To verify its effectiveness, HHOSA was benchmarked against other CI algorithms, including the Slime Mould Algorithm (SMA), Firefly Algorithm (FA), Manta Ray Foraging Optimization (MRFO), and Cuckoo Search Algorithm (COA). The evaluation considered the algorithm's stability, local search capability, convergence rate, computation time, and required population size. Findings suggest that the HHOSA outperforms its peers, marking it as a potential leader for designing bifacial PV power plants. The results indicate that the HHOSA algorithm exhibits superior performance in these aspects, making it a promising approach for optimizing the design of bifacial PV power plants. Moreover, this study provides insights into the economic and technical viability of bifacial PV systems under various environmental and system conditions. A sensitivity analysis, focusing on the interplay of three decision variables − albedo values (25 %, 50 %, and 75 %), tilt angles (10°, 25°, and 35°), and module elevations (0.5 m, 1.5 m, and 2 m) − was conducted. It assessed their influence on final yield, additional bifacial PV module yield, Levelized Cost of Electricity, and the system's Net Present Value. The results emphasize the importance of carefully considering the impacts of albedo, module elevation, and tilt angle on the financial performance of bifacial PV installations.
  • 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
    Cost of energy losses analysis using a hybrid evolutionary programming-firefly algorithm for distributed generation installation
    ( 2022)
    Noor Najwa Husnaini Mohammad Husni
    ;
    Siti Rafidah Abdul Rahim
    ;
    Mohd Rafi Adzman
    ;
    Muhammad Hatta Hussain
    ;
    Ismail Musirin
    This paper presents the Hybrid Evolutionary Programming-Firefly Algorithm (EPFA) technique for the cost of energy losses analysis of distributed generation (DG). In this study, EPFA is developed to determine the optimal size of DG while considering the system’s energy losses. EPFA is developed based on embedded Firefly Algorithm (FA) properties into the classical EP technique. The objective of this study was to reduce the cost of energy losses while increasing the voltage profile and minimizing distribution system losses between the different operational strategies and types of DG. In this study, the analysis was done by considering DG type 1 and DG type 2. The proposed technique was tested using the IEEE 69-bus test system. In terms of economic concerns, power system planners can use the information acquired for utility planning to determine the right location and capacity of DG. Finally, the proposed method can determine the appropriate DG sizing while reducing the cost of energy losses and total losses in the system, based on the simulation results.
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