基于不确定风集成场景的电力系统FACTS最优配置的正弦和虫洞能量鲸优化算法

IF 5.8 3区 计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY
Sunilkumar P. Agrawal, Pradeep Jangir,  Arpita, Sundaram B. Pandya, Anil Parmar, Ahmad O. Hourani, Bhargavi Indrajit Trivedi
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引用次数: 0

摘要

正弦和虫洞能量鲸优化算法(SWEWOA)代表了一种解决柔性交流输电系统(FACTS)器件(包括晶闸管控制串联补偿器(TCSC)、晶闸管控制移相器(TCPS)和静态无功补偿器(SVC))中最优潮流(OPF)问题的先进求解方法。SWEWOA通过整合正弦和虫洞能量特征,扩展了Whale Optimization Algorithm (WOA),从而提高了复杂非线性OPF问题的有效收敛的勘探和开发能力。在IEEE-30总线测试系统上,通过静态和动态加载场景对SWEWOA进行了性能评估,结果表明其优于五种当代算法:自适应混沌WOA (ACWOA)、混沌WOA、混沌WOA (CWOA)、正弦余弦算法差分进化(SCADE)和混合灰狼优化(HGWO)。研究表明,与其他算法相比,SWEWOA的发电成本降低幅度最小可达0.9% better performance. SWEWOA demonstrates superior power loss performance by achieving (\(\:{P}_{\text{loss,min}}\)) at the lowest level compared to all other tested algorithms which leads to better system energy efficiency. The dynamic loading performance of SWEWOA leads to a 4.38% reduction in gross costs which proves its capability to handle different operating conditions. The algorithm achieves top performance in Friedman Rank Test (FRT) assessments through multiple performance metrics which verifies its consistent reliability and strong stability during changing power demands. The repeated simulations show that SWEWOA generates mean costs (\(\:{C}_{\text{gen,min}}\)) and mean power loss values (\(\:{P}_{\text{loss,min}}\)) with small deviations which indicate its capability to maintain cost-effective solutions in each simulation run. SWEWOA demonstrates great potential as an advanced optimization solution for power system operations through the results presented in this study.
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Sine and Wormhole Energy Whale Optimization Algorithm for Optimal FACTS Placement in Uncertain Wind Integrated Scenario Based Power Systems

A Sine and Wormhole Energy Whale Optimization Algorithm for Optimal FACTS Placement in Uncertain Wind Integrated Scenario Based Power Systems

A Sine and Wormhole Energy Whale Optimization Algorithm for Optimal FACTS Placement in Uncertain Wind Integrated Scenario Based Power Systems

The Sine and Wormhole Energy Whale Optimization Algorithm (SWEWOA) represents an advanced solution method for resolving Optimal Power Flow (OPF) problems in power systems equipped with Flexible AC Transmission System (FACTS) devices which include Thyristor-Controlled Series Compensator (TCSC), Thyristor-Controlled Phase Shifter (TCPS), and Static Var Compensator (SVC). SWEWOA expands Whale Optimization Algorithm (WOA) through the integration of sine and wormhole energy features thus improving exploration and exploitation capabilities for efficient convergence in complex non-linear OPF problems. A performance evaluation of SWEWOA takes place on the IEEE-30 bus test system through static and dynamic loading scenarios where it demonstrates better results than five contemporary algorithms: Adaptive Chaotic WOA (ACWOA), WOA, Chaotic WOA (CWOA), Sine Cosine Algorithm Differential Evolution (SCADE), and Hybrid Grey Wolf Optimization (HGWO). The research shows that SWEWOA delivers superior generation cost reduction than other algorithms by reaching a minimum of 0.9% better performance. SWEWOA demonstrates superior power loss performance by achieving (\(\:{P}_{\text{loss,min}}\)) at the lowest level compared to all other tested algorithms which leads to better system energy efficiency. The dynamic loading performance of SWEWOA leads to a 4.38% reduction in gross costs which proves its capability to handle different operating conditions. The algorithm achieves top performance in Friedman Rank Test (FRT) assessments through multiple performance metrics which verifies its consistent reliability and strong stability during changing power demands. The repeated simulations show that SWEWOA generates mean costs (\(\:{C}_{\text{gen,min}}\)) and mean power loss values (\(\:{P}_{\text{loss,min}}\)) with small deviations which indicate its capability to maintain cost-effective solutions in each simulation run. SWEWOA demonstrates great potential as an advanced optimization solution for power system operations through the results presented in this study.

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来源期刊
Journal of Bionic Engineering
Journal of Bionic Engineering 工程技术-材料科学:生物材料
CiteScore
7.10
自引率
10.00%
发文量
162
审稿时长
10.0 months
期刊介绍: The Journal of Bionic Engineering (JBE) is a peer-reviewed journal that publishes original research papers and reviews that apply the knowledge learned from nature and biological systems to solve concrete engineering problems. The topics that JBE covers include but are not limited to: Mechanisms, kinematical mechanics and control of animal locomotion, development of mobile robots with walking (running and crawling), swimming or flying abilities inspired by animal locomotion. Structures, morphologies, composition and physical properties of natural and biomaterials; fabrication of new materials mimicking the properties and functions of natural and biomaterials. Biomedical materials, artificial organs and tissue engineering for medical applications; rehabilitation equipment and devices. Development of bioinspired computation methods and artificial intelligence for engineering applications.
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