熵驱动积分分数粒子群优化-重力搜索算法优化探索定向过电流继电器的最优协调

IF 4 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Asim Iqbal , Yasir Muhammad , Saeed Ehsan Awan , Bakht Muhammad Khan , Muhammad Asif Zahoor Raja
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引用次数: 0

摘要

保护系统是所有电力网络子系统(包括发电、输电和配电网络的保护系统)的重要组成部分,目的是确保发电机、母线、变压器和馈电线路等电力系统组件的完整性,因此保护系统中采用了不同类型的继电保护装置组合,即防止相关系统中的过流、线对地、线对线、双线对地故障。在当前的研究中,在标准电力系统保护过程中,通过减少总运行时间,包括定向过流继电器(DOCR)运行时间和主备定向过流继电器之间的协调时间,同时将间隔协调时间(CTI)、拾取分接设定(PTS)和时间刻度盘设定(TDS)保持在可接受的范围内,从而提高传统电力系统保护的性能。为了减少 IEEE 3 总线、8 总线和 15 总线系统中的适配性评估函数,设计了一种称为分数粒子群优化引力搜索算法熵指标(FPSOGSA-EM)的新方法,用于确定 CTI、PTS 和 TDS 的最佳设置。FPSOGSA-EM 将分数导数的基本理论纳入了典型粒子群优化的数学框架,并辅以引力搜索算法和熵指标,以提高其收敛速度并避免次优化。FPSOGSA-EM 的结果与其他前沿算法的结果进行了比较,如修正粒子群优化、修正水循环技术、修正电磁场优化、增强灰狼优化、搜索器算法、基于教学学习的优化、和谐搜索算法和 FPSOGSA。通过大幅缩短 DOCR 在传统 IEEE 3 总线、8 总线和 15 总线测试系统中的运行周期,FPSOGSA-EM 的性能优于之前介绍的这些方法。该方案的一致性、鲁棒性、优化效果、可靠性和稳定性通过统计解释得以确定,如最小适配性演化、累积分布函数(CDF)、方框图表示法、直方图和量子-量子图演示,作为中心倾向和多样性的衡量标准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Entropy driven integrated fractional particle swarm optimization - gravitational search algorithm optimization expedition for optimal coordination of directional over current relays
The protective system is an essential part of all power network subsystems, including the protection systems of generation, transmission, and distribution networks, in order to ensure the integrity of the power system components, such as generators, bus bars, transformers, and feeder lines thus, a combination of different types of protection relays is utilized in the protection system, i.e., the prevention of the overcurrent, line to ground, line to line, double line to ground, faults in the associated system. In the current study, the performance of legacy power system protection is enhanced by means of reducing the total time of operation, including the directional over current relay (DOCR) operating time and coordination time among primary and backup DOCRs, while keeping the coordination time of interval (CTI), pickup tap setting (PTS), and time dial setting (TDS) within acceptable limits, during the protection of standard power system. In order to reduce the fitness evaluation function in IEEE 3-bus, 8-bus, and 15-bus systems, a new approach called fractional particle swarm optimization gravitational search algorithm entropy metric (FPSOGSA-EM) is designed for determining the optimal settings of the CTI, PTS, and TDS. The FPSOGSA-EM incorporates the underlying theories of fractional derivatives inside the mathematical framework of canonical particle swarm optimization aided with gravitational search algorithm along with entropy metric to improve its convergence rate and avoid sub optimality. The yielded results from FPSOGSA-EM are compared to those from other cutting-edge counterpart algorithms such as modified particle swarm optimization, modified water cycle technique, modified electromagnetic field optimization, enhanced grey wolf optimization, seeker algorithm, teaching learning-based optimization, harmony search algorithm and FPSOGSA. By sharply reducing the period of operation of DOCRs in traditional IEEE 3-bus, 8-bus, and 15-bus test systems, the FPSOGSA-EM has outperformed these previously described methods. While the consistency, robustness, optimization brilliance, reliability and stability of the proposed scheme are ascertained by means of statistical interpretations such as minimum fitness evolution, cumulative distribution function (CDF), Boxplot representations, histograms plots and quantile–quantile plot demonstrations as a measure of center tendency and diversity.
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来源期刊
Computers & Electrical Engineering
Computers & Electrical Engineering 工程技术-工程:电子与电气
CiteScore
9.20
自引率
7.00%
发文量
661
审稿时长
47 days
期刊介绍: The impact of computers has nowhere been more revolutionary than in electrical engineering. The design, analysis, and operation of electrical and electronic systems are now dominated by computers, a transformation that has been motivated by the natural ease of interface between computers and electrical systems, and the promise of spectacular improvements in speed and efficiency. Published since 1973, Computers & Electrical Engineering provides rapid publication of topical research into the integration of computer technology and computational techniques with electrical and electronic systems. The journal publishes papers featuring novel implementations of computers and computational techniques in areas like signal and image processing, high-performance computing, parallel processing, and communications. Special attention will be paid to papers describing innovative architectures, algorithms, and software tools.
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