Optimizing transmission line parameter estimation with hybrid evolutionary techniques

IF 2 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Muhammad Suhail Shaikh, Saurav Raj, Shah Abdul Latif, Wulfran Fendzi Mbasso, Salah Kamel
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引用次数: 0

Abstract

Power flow, planning, economics, dispatch, and stability analysis rely on accurate transmission line parameters (TLPE). Standard optimization methods are employed to develop such analyses and obtain TLPE. Additionally, these methods have limitations, including precision, accuracy, and time complexity. It is challenging to find improved solutions using standard optimization methods due to slow convergence and limitations in identifying local optima. Concerned with these challenges, the study suggest a new application for an effective hybrid optimization method capable of addressing such limitations. The hybrid algorithm, named the Salp Swarm Algorithm with Sine Cosine Algorithm (HSSASCA), that aims to tackle the issues of slow convergence and local optima. The Sine Cosine Algorithm (SCA) is employed after the Salp Swarm Algorithm (SSA), and Salp integration is utilized to successfully explore and analyze the search space. To enhance the performance of HSSASCA, the hybrid technique aims to provide expanded exploration capabilities, effective exploitation of the search space, and a better convergence rate. These key features position the HSSASCA algorithm as an effective solution to complex optimization problems. To assess the efficiency of the HSSASCA algorithm, six different test systems are employed. Initially, the evaluation of exploration, exploitation, and minimized local optima is conducted using the CEC 2019 benchmark functions. Secondly, efficiency monitoring and verification of HSSASCA across different scenarios occur by comparing it with established optimization algorithms such as SSA, SCA, firefly optimization algorithm (FFO), Grey Wolf Optimization (GWO), student psychology-based optimization (SPBO), and Symbiotic Organisms Search (SOS). Finally, statistical analysis is performed, revealing that the HSSASCA outperforms SSA, SCA, FFO, GWO, SPBO, and SOS. In terms of statistical results and convergence curves, the HSSASCA demonstrates superior performance in searching efficiency, convergence accuracy, and local optimum avoidance ability.

Abstract Image

利用混合进化技术优化输电线路参数估计
电力流、规划、经济、调度和稳定性分析都依赖于精确的输电线路参数(TLPE)。标准优化方法可用于开展此类分析并获得 TLPE。此外,这些方法也有局限性,包括精度、准确性和时间复杂性。由于收敛速度慢和识别局部最优的局限性,使用标准优化方法寻找改进的解决方案具有挑战性。针对这些挑战,研究提出了一种新的应用方法,即一种能够解决这些局限性的有效混合优化方法。这种混合算法被命名为 Salp Swarm 算法与正余弦算法(HSSASCA),旨在解决收敛速度慢和局部最优的问题。在萨尔普群算法(SSA)之后采用了正余弦算法(SCA),并利用萨尔普集成成功地探索和分析了搜索空间。为了提高 HSSASCA 的性能,混合技术旨在提供更强的探索能力、对搜索空间的有效利用以及更好的收敛速度。这些关键特性使 HSSASCA 算法成为复杂优化问题的有效解决方案。为了评估 HSSASCA 算法的效率,我们采用了六个不同的测试系统。首先,使用 CEC 2019 基准函数对探索、利用和最小化局部最优进行评估。其次,通过与 SSA、SCA、萤火虫优化算法(FFO)、灰狼优化(GWO)、基于学生心理的优化(SPBO)和共生有机体搜索(SOS)等成熟的优化算法进行比较,对 HSSASCA 在不同场景下的效率进行监测和验证。最后,进行了统计分析,结果显示 HSSASCA 优于 SSA、SCA、FFO、GWO、SPBO 和 SOS。从统计结果和收敛曲线来看,HSSASCA 在搜索效率、收敛精度和局部最优回避能力方面都表现出了卓越的性能。
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来源期刊
Iet Generation Transmission & Distribution
Iet Generation Transmission & Distribution 工程技术-工程:电子与电气
CiteScore
6.10
自引率
12.00%
发文量
301
审稿时长
5.4 months
期刊介绍: IET Generation, Transmission & Distribution is intended as a forum for the publication and discussion of current practice and future developments in electric power generation, transmission and distribution. Practical papers in which examples of good present practice can be described and disseminated are particularly sought. Papers of high technical merit relying on mathematical arguments and computation will be considered, but authors are asked to relegate, as far as possible, the details of analysis to an appendix. The scope of IET Generation, Transmission & Distribution includes the following: Design of transmission and distribution systems Operation and control of power generation Power system management, planning and economics Power system operation, protection and control Power system measurement and modelling Computer applications and computational intelligence in power flexible AC or DC transmission systems Special Issues. Current Call for papers: Next Generation of Synchrophasor-based Power System Monitoring, Operation and Control - https://digital-library.theiet.org/files/IET_GTD_CFP_NGSPSMOC.pdf
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