Optimal Configuration of Fault Location Measurement Points in DC Distribution Networks Based on Improved Particle Swarm Optimization Algorithm

The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization algorithm.Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing.The model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach.

Angular insensitive nonreciprocal ultrawide band absorption in plasma-embedded photonic crystals designed with improved particle swarm optimization algorithm

Using an improved particle swarm optimization algorithm(IPSO)to drive a transfer matrix method,a nonreciprocal absorber with an ultrawide absorption bandwidth and angular insensitivity is realized in plasma-embedded photonic crystals arranged in a structure composed of periodic and quasi-periodic sequences on a normalized scale.The effective dielectric function,which determines the absorption of the plasma,is subject to the basic parameters of the plasma,causing the absorption of the proposed absorber to be easily modulated by these parameters.Compared with other quasi-periodic sequences,the Octonacci sequence is superior both in relative bandwidth and absolute bandwidth.Under further optimization using IPSO with 14 parameters set to be optimized,the absorption characteristics of the proposed structure with different numbers of layers of the smallest structure unit N are shown and discussed.IPSO is also used to address angular insensitive nonreciprocal ultrawide bandwidth absorption,and the optimized result shows excellent unidirectional absorbability and angular insensitivity of the proposed structure.The impacts of the sequence number of quasi-periodic sequence M and collision frequency of plasma1ν1 to absorption in the angle domain and frequency domain are investigated.Additionally,the impedance match theory and the interference field theory are introduced to express the findings of the algorithm.

Solving Job-Shop Scheduling Problem Based on Improved Adaptive Particle Swarm Optimization Algorithm

An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms.

Speed Control of Motor Based on Improved Glowworm Swarm Optimization

To better regulate the speed of brushless DC motors,an improved algorithm based on the original Glowworm Swarm Optimization is proposed.The proposed algorithm solves the problems of poor robustness,slow convergence,and low accuracy exhibited by traditional PID controllers.When selecting the glowworm neighborhood set,an optimization scheme based on the growth and competition behavior of weeds is applied to a single glowworm to prevent falling into a local optimal solution.After the glowworm’s position is updated,the league selection operator is introduced to search for the global optimal solution.Combining the local search ability of the invasive weed optimization with the global search ability of the league selection operator enhances the robustness of the algorithm and also accelerates the convergence speed of the algorithm.The mathematical model of the brushless DC motor is established,the PID parameters are tuned and optimized using improved Glowworm Swarm Optimization algorithm,and the speed of the brushless DC motor is adjusted.In a Simulink environment,a double closed-loop speed control model was established to simulate the speed control of a brushless DC motor,and this simulation was compared with a traditional PID control.The simulation results show that the model based on the improved Glowworm Swarm Optimization algorithm has good robustness and a steady-state response speed for motor speed control.

Study of Direction Probability and Algorithm of Improved Marriage in Honey Bees Optimization for Weapon Network System

To solve the weapon network system optimization problem against small raid objects with low attitude,the concept of direction probability and a new evaluation index system are proposed.By calculating the whole damaging probability that changes with the defending angle,the efficiency of the whole weapon network system can be subtly described.With such method,we can avoid the inconformity of the description obtained from the traditional index systems.Three new indexes are also proposed,i.e.join index,overlap index and cover index,which help manage the relationship among several sub-weapon-networks.By normalizing the computation results with the Sigmoid function,the matching problem between the optimization algorithm and indexes is well settled.Also,the algorithm of improved marriage in honey bees optimization that proposed in our previous work is applied to optimize the embattlement problem.Simulation is carried out to show the efficiency of the proposed indexes and the optimization algorithm.

Dynamic Self-Adaptive Double Population Particle Swarm Optimization Algorithm Based on Lorenz Equation

In order to improve some shortcomings of the standard particle swarm optimization algorithm, such as premature convergence and slow local search speed, a double population particle swarm optimization algorithm based on Lorenz equation and dynamic self-adaptive strategy is proposed. Chaotic sequences produced by Lorenz equation are used to tune the acceleration coefficients for the balance between exploration and exploitation, the dynamic self-adaptive inertia weight factor is used to accelerate the converging speed, and the double population purposes to enhance convergence accuracy. The experiment was carried out with four multi-objective test functions compared with two classical multi-objective algorithms, non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results show that the proposed algorithm has excellent performance with faster convergence rate and strong ability to jump out of local optimum, could use to solve many optimization problems.

Improved algorithms to plan missions for agile earth observation satellites

This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.

改进灰狼算法优化GBDT在PM_(2.5)预测中的应用

针对灰狼算法易陷入局部最优解和全局搜索能力不足的问题,通过霍尔顿序列(Halton Sequence)搜索算法初始化狼群位置,避免灰狼算法陷入局部最优解和重复运算;引入莱维飞行和随机游动策略对灰狼算法的寻优过程进行优化,以增加算法的全局搜索能力;利用粒子群算法模拟灰狼种群得出的最佳适应度以用于惩罚项改进灰狼算法中的头狼更新策略。使用改进算法优化的梯度提升树(Gradient Boosting Decision Trees,GBDT)模型对北京市大气污染物监测数据中PM_(2.5)质量浓度进行预测,采用3种评估函数对各模型以及混合模型预测效果得分进行评估。结果显示,本文改进的灰狼算法对梯度提升树的优化效果优于其他算法,均方根误差E RMS为6.65μg/m^(3),平均绝对值误差E MA为3.20μg/m^(3),拟合优度(R^(2))为99%,比传统灰狼算法优化结果的均方根误差减少了19.19μg/m^(3),平均绝对值误差降低了10.03μg/m^(3),拟合优度增加了9百分点;与霍尔顿序列和莱维飞行改进的(Levy Flight-Halton Sequence,LHGWO)相比,改进的灰狼算法预测得分的均方根误差降低了10.39μg/m^(3),平均绝对值误差减小了6.71μg/m^(3),拟合优度提高了5百分点。研究表明了预测模型优化的有效性,为未来城市改善空气质量提供了科学依据和技术支持。

基于改进引力搜索算法的水轮机调节系统仿真

针对现阶段水电机组存在多种复杂工况、工程计算受限于算法本身的复杂性等问题,提出一种改进的引力搜索算法(改进PSOGSA),以此提高水轮机控制参数的优化性能,弥补传统控制策略难以满足动态需求的不足.首先,结合PSO算法,在GSA的速度更新公式中引入学习因子进行改进.其次,应用一种权重系数优化其位置更新公式,提高算法的自适应性.最后,结合相关仿真建模试验,使用所提改进PSOGSA对水轮机调节系统PID参数进行优化调节.仿真结果表明,在5%空载频率扰动下,改进PSOGSA的PID控制器明显优于上述传统算法,所调节的模型系统能在更短时间内趋于稳定,此时的超调量远低于传统算法,表明此改进PSOGSA在后续迭代中具备更高的迭代效率,并且改善了常规算法中易陷入局部最优的问题,从而证明了改进PSOGSA的合理有效性,水轮机调节系统的控制效果在一定程度上得到优化.

考虑碳排放的分布式电源优化配置

对分布式电源接入配电网进行合理的优化配置,能在兼顾运营商和用户利益的同时,改善系统整体电压分布。建立了综合考虑分布式电源投资成本、用户购电成本、网损费用和碳排放费用的多目标优化模型。利用改进层次分析法确定各目标的权重,进而转化为单目标函数规划问题。针对天牛须算法个体单一性在解决高维复杂问题时精度低,优化效果不佳的问题,提出了一种改进天牛须粒子群算法,利用混沌映射对参数进行调整,引入动态惯性权重、莱维飞行机制,提高了收敛速度。以IEEE33节点系统为例,将改进天牛须粒子群算法与粒子群算法及天牛须粒子群算法的效果对比,验证改进算法对分布式电源优化配置问题的可行性,有效降低了碳排放费用、用户购电费用,减少了系统网损,改善了系统整体电压分布。

采用改进多目标粒子群算法的斜拉桥阻尼器参数优化

为克服大跨度斜拉桥黏滞阻尼器优化设计效率低、多个相互制约的减震控制目标的问题难以权衡,基于遗传算法的“变异”方法,提出了改进多目标粒子群算法来进行阻尼器参数优化设计。建立大跨度斜拉桥的有限元模型,开展了全桥地震响应分析,根据抗震需求在桥梁纵向设置黏滞阻尼器;分别建立了塔底弯矩、阻尼力和梁端位移的减震响应与阻尼器参数之间的响应面数学模型;以减震响应面模型为研究对象,通过该算法进行阻尼器参数全局自动寻优分析,确定了阻尼器的最优参数,并与采用参数敏感性分析方法确定的一组阻尼参数进行对比分析。研究结果表明:该优化方法具有计算精度好、优化效率高和更好地权衡多个相互制约的减震控制目标的优点;通过优化算法获得的阻尼器参数组合相比采用参数敏感性分析方法获得的阻尼参数组合的减震响应,塔底弯矩增大1.73%,阻尼力减小5.97%,梁端位移减小1.66%;在无需多次有限元试算的基础上确定了更高精度的阻尼器优化参数组合,在提高减震效果的同时大大提升了计算效率。

电动汽车双层优化模型的充放电调度策略

传统的分时电价策略虽然一定程度上可以改善电动汽车无序充电所产生的电网日负荷峰谷差加大、负荷率降低等状况,但易产生新的负荷高峰,并且当前多目标优化等策略削峰填谷效果欠佳或用户参与度不高。针对上述问题,提出一种基于双层优化模型的调度策略以充分考虑电网和用户两侧需求。第1层模型以优化电网日负荷方差最小为目标函数;第2层优化模型建立以车主充电成本最小以及保证用户出行需求的目标函数,然后用改进的粒子群-模拟退火算法对双层优化模型进行循环迭代求解,并将第2层优化后的结果反馈给第1层,以此循环优化,输出最终结果。对比优化前后的负荷曲线,结果表明:与当前优化策略相比,所提出的基于双层优化模型的V2G调度策略能有效降低新的负荷高峰及负荷峰谷差,减少参与V2G的用户成本,实现两侧双赢。

采用改进BP-PID控制的机器人避障仿真研究

针对移动机器人避障过程中行驶路径长、寻路速度慢等问题,提出了一种改进反向传播-比例-积分-微分(BP-PID)控制器,并对移动机器人避障效果进行仿真验证。利用移动机器人在二维坐标系的避障简图,得出了移动机器人运动方程式。引用比例-积分-微分(PID)控制器和3层BP神经网络结构,利用BP神经网络的学习能力调整PID控制器参数。引用粒子群算法进行改进,通过改进粒子群算法在线优化BP-PID控制器,确保移动机器人BP-PID控制器收敛于全局最优值,从而使移动机器人避障效果更好。在不同环境中,采用Matlab软件对移动机器人避障效果进行仿真,比较改进前和改进后的移动机器人避障效果。结果显示:在不同环境中,改进前和改进后的BP-PID控制器均能使移动机器人安全地躲避障碍物;但是采用改进的粒子群算法优化BP-PID控制器,可以使移动机器人运动路径更短,迭代次数更少,搜索时间更短。采用改进BP-PID控制器,能够提高移动机器人避障过程中寻路速度,缩短行驶路径,效果更好。

基于空海异构无人平台的水下目标搜索与跟踪

海上异构无人系统可有效提高复杂任务的完成效率。文中采用自主水下航行器(AUV)和无人机(UAV)来完成近海海域内未知水下目标的搜索与跟踪任务。首先,描述了水下目标搜索跟踪任务,将任务过程分为目标搜索和目标跟踪阶段,2个阶段的目标分别是使AUV&UAV总搜索空间最大化以及AUV与水下目标的末端位置误差最小;然后,建立AUV&UAV跨域协同搜索模型,并设定模型中AUV和UAV探测范围和通信距离等约束条件;最后,在跨域协同搜索与路径跟踪规划中,基于传统粒子群算法,加入自适应学习因子调控策略和精英保存策略,生成搜索与跟踪路径。仿真实验表明,采用改进粒子群优化算法的AUV&UAV异构无人系统能够更高效地完成水下目标搜索与跟踪任务。

面向造纸污水处理的故障诊断复合算法研究

故障诊断是保障系统稳定性与安全性的关键节点。在造纸污水处理过程中,系统硬件设备由于长期处于恶劣环境极易引发系统故障,因此准确诊断故障以避免不可挽回的损失至关重要。基于此,针对造纸污水处理过程的特点,以主成分分析技术提取故障主元而明确故障诊断模型输入量,以粒子群优化算法优化机器学习算法支持向量机而构成故障诊断复合算法,由此搭建了面向造纸污水处理的故障诊断模型,并进行了仿真分析。结果发现,面向造纸污水处理的故障诊断复合算法正确率可达96.9%,且稳定性与鲁棒性较高,可广泛推广至多工业污水处理领域。

风电数据的不确定性建模及在电网规划的应用

在我国经济高速发展的同时,矿物资源的使用持续增长,对环境的污染也在不断加剧,发展风力发电是我国实现低碳转型的一项重要措施。然而,由于风力发电具有较强的不稳定性,这给电网的运行带来了较大的不确定性。因此,考虑风力发电过程中的不确定性因素,并对其进行建模,开展含风力发电的电网规划研究。首先对风电出力的不确定性进行建模,建立了风电机组出力的数学模型。其次,提出了以总成本、总网损最小为目标函数的考虑风电不确定性的最优潮流模型,并给出一种采用局部模型并引入动态惯性权重系数改进的粒子群优化求解算法。经采用实际的风电数据进行实验,结果表明与传统的粒子群优化算法相比,改进的粒子群优化算法在求解速度、收敛性以及稳健性方面均具有更优性能。

基于改进粒子群算法的标签天线结构参数多目标优化设计

为了解决天线设计人员应用电磁仿真软件优化天线结构时存在的优化方向不明确和优化速度慢的问题,文中以干式水表的嵌入式射频识别标签天线设计为例,提出了基于改进粒子群算法的标签天线结构参数多目标寻优方法。首先,根据干式水表产品追溯需求,提出了中心频点尽可能接近理想中心频点、回波损耗尽可能低、带宽尽可能宽、面积尽可能小的四个目标函数。其次,为避免粒子群算法陷入局部最优,采用多维均匀拉丁超立方初始化、Logistic混沌映射非线性变化惯性权重、网格划分变化学习因子、高斯扰动策略等方法对算法进行改进,并应用于标签天线结构参数多目标优化中。最后,进行了实例验证。验证结果表明:利用改进后的粒子群算法得到的标签天线结构参数优化结果可更大程度满足优化目标需求,优化耗时仅为电磁仿真软件的40.1%。

基于IPSO-BP的船舶航迹预测研究

目的面对复杂的海上交通及密集的物流交通流,及时有效地对船舶航迹进行跟踪预测显得尤为重要,针对传统船舶航迹预测方法精确度低且效率低下的问题,提出一种改进方法。方法在船舶自动识别系统(Automatic Identification System,AIS)数据的基础上,建立改进粒子群算法(IPSO)与BP神经网络相结合的船舶轨迹预测模型,利用船舶历史航行轨迹数据,实现对未来船舶运动的预测。选取宁波舟山港的船舶历史轨迹数据进行实验,并将IPSO-BP模型的实验结果与其他模型进行比较。结果不同模型航迹预测对比结果表明,IPSO-BP模型的性能较好,其预测精度较高,适用于船舶轨迹预测。结论使用IPSO-BP模型能够更加精准地预测船舶航迹,在船舶危险预警、船舶异常监测等方面具有重要的指导作用。

基于改进粒子群算法的UWB雷达人体动作识别研究

针对雷达信号中的杂波干扰及样本数量对人体动作识别精度的限制,提出一种基于改进粒子群算法(particle swarm optimization,PSO)优化支持向量机(support vector machine,SVM)模型的超宽带(ultra-wideband,UWB)雷达人体动作识别算法。利用动态目标指示(moving target indication,MTI)与小波阈值滤波对接收到的UWB回波信号进行预处理,消除回波信号中的杂波和噪声对人体动作识别的影响;结合二维离散小波包分解(two dimensional discrete wavelet packet decomposition,2D-DWPD)与奇异值分解(singular value decomposition,SVD),对预处理后的雷达信号进行特征提取和降维;提出一种改进粒子群算法,优化SVM模型的相关参数进行识别和分类。实验结果表明,提出的算法准确率可达到96.25%,具有良好的识别性能。

改进PSO算法在断层滑动参数反演中的研究与应用

利用地面大地测量数据反演断层的滑动速率等动态参数,是大地测量主要研究问题之一。本文首先提出了一种改进的粒子群算法,以此弥补标准粒子群算法可能局部最优解的不足,并通过模拟数据进行试验验证。然后以渭河盆地两条主要断裂为研究对象,利用地面GPS观测数据反演了秦岭北侧大断裂、临潼-长安断裂的三维滑动速率,并分析了两种算法的运行耗时。结果表明:改进的粒子群算法比标准粒子群算法耗时减少,收敛速度更快;本文所提算法反演得到的断层参数更符合真实的断裂条件,具有一定的实际应用价值。