Siti Rafidah Abdul Rahim
Thumbnail Image
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

Research Output

Filters

6 2
6
5 1
Reset filters

Settings
Now showing 1 - 6 of 6
Thumbnail Image
Publication

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.

View
Thumbnail Image
Publication

Grid integration of multiple PV inverters with reduced number of interfacing transformers— A dedicated controller for elimination of DC current injection

2023-03-01 , Syahrul Ashikin Azmi , Adam G.P. , Williams B.W. , Rahim N.A. , Siti Rafidah Abdul Rahim

The injection of dc current offset into ac networks may impacted the lifespan of the distribution system equipment including isolation transformers and measurement devices and in serious event may cause equipment's malfunction. This paper intents to eliminate dc current offsets in the output currents using a combinational of proportional-integral (PI) and proportional resonance (PR) controls embedded in one inverter unit. Resultant output currents of this method are sinusoidal and clean from dc offset before entering the point of common coupling. This method gives advantages for transformerless option for group of interfacing inverters in the medium-scale solar farm or in arrangement of inverters restricted in a small locale. Moreover, the use of expensive and high-accuracy measurement sensor nor complex transformer can be omitted, whilst indirectly give positive impact to the operational cost of the farm. The simulation verifications proved the usefulness of the proposed method by introducing varying unknown dc offset levels in the phase currents, and a dedicated dc offset suppressor inverter able to successfully eliminate the dc offset to zero. The validity of the proposed method is demonstrated in simulation using MATLAB/Simulink.

View
Thumbnail Image
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.

View
Thumbnail Image
Publication

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.

View
Thumbnail Image
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.

View
Thumbnail Image
Publication

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.

View