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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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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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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Comparative Evaluation of Three-Phase Inverter Topologies Based on Voltage Boosting Features

2023-01-01 , Yee C.S. , Syahrul Ashikin Azmi , Hwai L.J. , Mohammad Faridun Naim Tajuddin , Zahari M.Z.A. , Siti Rafidah Abdul Rahim

Voltage source inverter (VSI) is commonly used in industrial due to its stable operation and low cost. However, VSI needs to operate with an extra converter stage which is a DC-DC converter for voltage boosting purposes. In contrast, current source inverter (CSI) inherits voltage boosting features may become an alternative option to VSI. Yet, there were minimal research on CSI that dedicates to the voltage boosting features. This research focuses on comparing the voltage boosting features of CSI and VSI in both open-loop and closed-loop conditions. The performance of VSI and CSI are simulated using MATLAB/Simulink. Under open-loop operation, CSI produces a voltage boosting capability at approximately 55% higher than VSI. Yet, CSI suffers high THD percentage as compared to VSI for the same switching frequency. This high THD shortcoming can be easily resolved by using a simple CL filter. For closed-loop operation, VSI and CSI with voltage-controlled synchronous frame PI control systems are proven to have good reference tracking and harmonic rejection and are suitable to be implemented for household applications or for a standalone system. Interestingly, CSI closed-loop system can achieve a wider range of output due to the voltage boosting capability and provide a better quality of output waveform as compared to VSI.

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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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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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A Hybrid Optimization Approach for Power Loss Reduction and Voltage Profile Improvement in Distribution System

2022-01-01 , Noor Najwa Husnaini Mohammad Husni , Siti Rafidah Abdul Rahim , Mohd Rafi Adzman

In the past decades, the electrical power system is designed and developed to satisfy the owner demand that continuously appears in many variations. Hence, engineers have put their full effort to solve the problem associated with electrical power systems that come and might arise in the future. Therefore, distributed generation (DG) has been introduced to solve multiple electrical power system problems. The proposed methodology presented in this study focuses on minimizing network power losses, improving the voltage profile of system operation, and security constraints in a distribution. It is known that the location and capacity of DG play significant roles in the system losses in a distribution system. A hybrid metaheuristic nature-inspired algorithm is presented in this study for optimal location and sizing of multiple DG units. The best location and optimal sizing of DGs will be determined through Hybrid metaheuristic of Artificial Immune System Firefly Algorithm (AISFA). The designated technique will be tested into IEEE-69 test system using MATLAB software. For reducing the power losses, the simulation results have shown that bus 61 is the best location for reducing power losses and improving voltage profile in IEEE-69 test system in the preliminary result. By installing DG at bus 61, the real power losses improve about 89%, with a voltage profile improvement index up to 1.249099.

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Effect of installation of distributed generation at different points in the distribution system on voltage drops and power losses

2021-05-03 , Hasibuan A. , Muzamir Isa , Mohd Irwan Yusoff , Siti Rafidah Abdul Rahim , Nrartha I.M.A.

The main purpose of this paper is to analyze the impact of different positions of different DG penetrations on bus voltage profiles and channel power losses. The main classification can be done by putting DG on the most critical bus, the closest bus to the most critical but critical bus too, the closest bus to the feeder but critical too, and the furthest from the feeder but critical too. Installing a distributed generation in a distribution network can significantly affect the power system. The effect depends on DG allocation of the distribution network. Implementation of this approach has been made with the IEEE 34 bus standard at different points. DG placement site selection method based on buses decreased voltage over the limit permitted. Simulation results from a case study on the IEEE 34 bus standard system show that the voltage profile on each bus and the loss of system power will be different when compared to the DG installation at different points.

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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.

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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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