In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for n...In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network optimization.This study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic RESs.The primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss mitigation.Additionally,a carbon tax is included in the objective function to reduce carbon emissions.Thorough scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal solutions.Simulation results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution quality.Notably,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)effectively.This research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local minima.GBBLCO emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.展开更多
针对已有的算法在基于到达时间差(time difference of arrival,TDOA)测量方案中存在的搜索能力不均衡,导致三维定位区域局部存在定位精度低甚至求解失败的问题,提出了一种基于改进探路者优化算法(pathfinder algorithm,PFA)的TDOA定位算...针对已有的算法在基于到达时间差(time difference of arrival,TDOA)测量方案中存在的搜索能力不均衡,导致三维定位区域局部存在定位精度低甚至求解失败的问题,提出了一种基于改进探路者优化算法(pathfinder algorithm,PFA)的TDOA定位算法,通过将自适应Levy飞行和改进后的PFA算法进行融合,增强了个体对定位区域复杂环境的适应性,解决算法早熟、易陷入局部最优等问题,提升了算法综合性能.通过仿真和实验,结果表明:与Taylor算法、LM算法相比,本文提出的算法(Levy-pathfinder algorithm,LPFA)可以提高定位精度;与PSO算法、PFA算法相比,LPFA算法可以在提高运算速度的同时得到更准确的定位结果.展开更多
目前电动汽车动力输出的来源主要是动力电池,其荷电状态(State of Charge,SOC)表示电池的剩余电量情况,精确估算SOC对于电池的使用安全有重要意义。将蝴蝶优化算法(Butterfly Optimization Algorithm,BOA)进行改进并用于优化BP神经网络...目前电动汽车动力输出的来源主要是动力电池,其荷电状态(State of Charge,SOC)表示电池的剩余电量情况,精确估算SOC对于电池的使用安全有重要意义。将蝴蝶优化算法(Butterfly Optimization Algorithm,BOA)进行改进并用于优化BP神经网络估算动力电池SOC,解决了普通BP网络估计SOC时遇到的训练时间长、收敛慢、精度较低、易陷入局部最优解的问题;同时提升了全局搜索速度,选取电压和电流为输入变量、SOC为输出变量,根据误差的大小调整神经网络的权值和阈值。仿真结果表明,优化后得到的SOC估计结果误差率控制在1.1%以内,该方法寻优速度快,具有更好的鲁棒性。展开更多
在局部阴影条件下,光伏阵列P-U输出曲线上出现的多个峰值会导致传统功率算法控制效率的降低。在采用传统灰狼算法(Grey wolf optimization,GWO)实现光伏阵列最大功率点跟踪(Maximum power point tracking,MPPT)时,发现该算法存在全局搜...在局部阴影条件下,光伏阵列P-U输出曲线上出现的多个峰值会导致传统功率算法控制效率的降低。在采用传统灰狼算法(Grey wolf optimization,GWO)实现光伏阵列最大功率点跟踪(Maximum power point tracking,MPPT)时,发现该算法存在全局搜索能力不足和容易陷入局部最优值的缺陷;因此,为增加种群跳出局部最优的概率,针对GWO收敛因子,提出一种新颖的非线性更新策略——在种群的前期迭代过程中平衡全局搜索与局部搜索能力,在群体定位更新公式中引入Levy飞行与动态加权策略。MATLAB/Simulink仿真结果表明,所提出的复合型MPPT控制算法在各种局部遮阳、光照突变和温度变化的情况下都能够有效避免算法陷入局部最优,且在追踪成功率、精度和时间3个方面均表现出优于传统算法的优势。展开更多
With the recent increase in network attacks by threats,malware,and other sources,machine learning techniques have gained special attention for intrusion detection due to their ability to classify hundreds of features ...With the recent increase in network attacks by threats,malware,and other sources,machine learning techniques have gained special attention for intrusion detection due to their ability to classify hundreds of features into normal system behavior or an attack attempt.However,feature selection is a vital preprocessing stage in machine learning approaches.This paper presents a novel feature selection-based approach,Remora Optimization Algorithm-Levy Flight(ROA-LF),to improve intrusion detection by boosting the ROA performance with LF.The developed ROA-LF is assessed using several evaluation measures on five publicly available datasets for intrusion detection:Knowledge discovery and data mining tools competition,network security laboratory knowledge discovery and data mining,intrusion detection evaluation dataset,block out traffic network,Canadian institute of cybersecu-rity and three engineering problems:Cantilever beam design,three-bar truss design,and pressure vessel design.A comparative analysis between developed ROA-LF,particle swarm optimization,salp swarm algorithm,snake opti-mizer,and the original ROA methods is also presented.The results show that the developed ROA-LF is more efficient and superior to other feature selection methods and the three tested engineering problems for intrusion detection.展开更多
基金supported by the Deanship of Postgraduate Studies and Scientific Research at Majmaah University in Saudi Arabia under Project Number(ICR-2024-1002).
文摘In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network optimization.This study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic RESs.The primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss mitigation.Additionally,a carbon tax is included in the objective function to reduce carbon emissions.Thorough scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal solutions.Simulation results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution quality.Notably,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)effectively.This research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local minima.GBBLCO emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.
文摘目前电动汽车动力输出的来源主要是动力电池,其荷电状态(State of Charge,SOC)表示电池的剩余电量情况,精确估算SOC对于电池的使用安全有重要意义。将蝴蝶优化算法(Butterfly Optimization Algorithm,BOA)进行改进并用于优化BP神经网络估算动力电池SOC,解决了普通BP网络估计SOC时遇到的训练时间长、收敛慢、精度较低、易陷入局部最优解的问题;同时提升了全局搜索速度,选取电压和电流为输入变量、SOC为输出变量,根据误差的大小调整神经网络的权值和阈值。仿真结果表明,优化后得到的SOC估计结果误差率控制在1.1%以内,该方法寻优速度快,具有更好的鲁棒性。
文摘在局部阴影条件下,光伏阵列P-U输出曲线上出现的多个峰值会导致传统功率算法控制效率的降低。在采用传统灰狼算法(Grey wolf optimization,GWO)实现光伏阵列最大功率点跟踪(Maximum power point tracking,MPPT)时,发现该算法存在全局搜索能力不足和容易陷入局部最优值的缺陷;因此,为增加种群跳出局部最优的概率,针对GWO收敛因子,提出一种新颖的非线性更新策略——在种群的前期迭代过程中平衡全局搜索与局部搜索能力,在群体定位更新公式中引入Levy飞行与动态加权策略。MATLAB/Simulink仿真结果表明,所提出的复合型MPPT控制算法在各种局部遮阳、光照突变和温度变化的情况下都能够有效避免算法陷入局部最优,且在追踪成功率、精度和时间3个方面均表现出优于传统算法的优势。
文摘With the recent increase in network attacks by threats,malware,and other sources,machine learning techniques have gained special attention for intrusion detection due to their ability to classify hundreds of features into normal system behavior or an attack attempt.However,feature selection is a vital preprocessing stage in machine learning approaches.This paper presents a novel feature selection-based approach,Remora Optimization Algorithm-Levy Flight(ROA-LF),to improve intrusion detection by boosting the ROA performance with LF.The developed ROA-LF is assessed using several evaluation measures on five publicly available datasets for intrusion detection:Knowledge discovery and data mining tools competition,network security laboratory knowledge discovery and data mining,intrusion detection evaluation dataset,block out traffic network,Canadian institute of cybersecu-rity and three engineering problems:Cantilever beam design,three-bar truss design,and pressure vessel design.A comparative analysis between developed ROA-LF,particle swarm optimization,salp swarm algorithm,snake opti-mizer,and the original ROA methods is also presented.The results show that the developed ROA-LF is more efficient and superior to other feature selection methods and the three tested engineering problems for intrusion detection.
