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Iranian Journal of Electrical and Electronic Engineering، جلد ۲۱، شماره ۲، صفحات ۳۶۱۱-۳۶۱۱
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عنوان انگلیسی A Comparative Study on DG Placement Using Marine Predator and Osprey Algorithms to Enhance Loss Reduction Index in the Distribution System
چکیده انگلیسی مقاله 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.
کلیدواژه‌های انگلیسی مقاله Distributed Generation, Nature Inspired Algorithm, Marine Predator Algorithm, Osprey Optimization Algorithm, MATLAB, Power Distribution, IEEE 69-Bus, Loss Reduction Index.
نویسندگان مقاله | Syazwan Ahmad Sabri
Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Malaysia


| Siti Rafidah Abdul Rahim
Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Malaysia


| Azralmukmin Azmi
Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Malaysia


| Syahrul Ashikin Azmi
Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Malaysia


| Muhamad Hatta Hussain
Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Malaysia


| Ismail Musirin
School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, Selangor, Malaysia
نشانی اینترنتی http://ijeee.iust.ac.ir/browse.php?a_code=A-10-5489-1&slc_lang=en&sid=1
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زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده Artificial Intelligence Techniques
نوع مقاله منتشر شده Only For Articles of ELECRiS 2024
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