Broken Rotor Bar Fault Diagnosis of Induction Motors Using a Hybrid Bare-bones Particle Swarm Optimization Algorithm

在传统定子电流频谱分析中,感应电机转子断条故障特征经常被基波分量淹没而无法准确检测。针对该问题,提出一种基于混合骨干微粒群优化算法的转子断条故障诊断新方法。该方法首先根据电流信号与单位余弦基函数的内积最大准则,利用混合骨干微粒群算法强大的全局搜索能力,准确估计出基波波形参数;然后利用波形参数构造出基波表达式,并将其从原电流信号中剔除,达到突出故障特征的目的。针对微粒群算法在进化后期收敛缓慢的缺点,通过K–均值聚类方式,引入单纯形法对其进行改进,使整个算法的广度探索与深度开发能力得到了有效均衡。最后,对模拟数据和实测信号进行实验,结果验证了所提方法的有效性和优越性。

Broken Rotor Bar Fault Detection of Induction Motors Using a Joint Algorithm of Trust Region and Modified Bare-bones Particle Swarm Optimization

A precise detection of the fault feature parameter of motor current is a new research hotspot in the broken rotor bar(BRB) fault diagnosis of induction motors. Discrete Fourier transform(DFT) is the most popular technique in this field, owing to low computation and easy realization. However, its accuracy is often limited by the data window length, spectral leakage, fence e ect, etc. Therefore, a new detection method based on a global optimization algorithm is proposed. First, a BRB fault current model and a residual error function are designed to transform the fault parameter detection problem into a nonlinear least-square problem. Because this optimization problem has a great number of local optima and needs to be resolved rapidly and accurately, a joint algorithm(called TR-MBPSO) based on a modified bare-bones particle swarm optimization(BPSO) and trust region(TR) is subsequently proposed. In the TR-MBPSO, a reinitialization strategy of inactive particle is introduced to the BPSO to enhance the swarm diversity and global search ability. Meanwhile, the TR is combined with the modified BPSO to improve convergence speed and accuracy. It also includes a global convergence analysis, whose result proves that the TR-MBPSO can converge to the global optimum with the probability of 1. Both simulations and experiments are conducted, and the results indicate that the proposed detection method not only has high accuracy of parameter estimation with short-time data window, e.g., the magnitude and frequency precision of the fault-related components reaches 10^(-4), but also overcomes the impacts of spectral leakage and non-integer-period sampling. The proposed research provides a new BRB detection method, which has enough precision to extract the parameters of the fault feature components.

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.

Dynamic Multi-objective Optimization of Chemical Processes Using Modified BareBones MOPSO Algorithm

Dynamic multi-objective optimization is a complex and difficult research topic of process systems engineering. In this paper,a modified multi-objective bare-bones particle swarm optimization( MOBBPSO) algorithm is proposed that takes advantage of a few parameters of bare-bones algorithm. To avoid premature convergence,Gaussian mutation is introduced; and an adaptive sampling distribution strategy is also used to improve the exploratory capability. Moreover, a circular crowded sorting approach is adopted to improve the uniformity of the population distribution.Finally, by combining the algorithm with control vector parameterization,an approach is proposed to solve the dynamic optimization problems of chemical processes. It is proved that the new algorithm performs better compared with other classic multiobjective optimization algorithms through the results of solving three dynamic optimization problems.

密度峰值聚类在塔机损伤诊断中的应用研究

建立塔机有限元模型,获取塔机完好状态和各损伤工况的各采集点的动态位移。提出了两种模型建立方法,基于悬臂梁的双输入单输出模型和基于时域数据的动态双输入单输出模型,对基于时域数据的双输入单输出模型首先利用最小二乘法计算参数初值,进一步利用粒子群优化方法进行参数优化,提高了模型精度。以完好工况的塔机数据为基础,建立基于悬臂梁的双输入单输出模型和基于时域数据的双输入单输出模型,计算参数,建立损伤识别模型,用待检状态的位移数值拟合模型,用两种模型计算出的残差方差做损伤因子,利用密度峰值聚类方法对损伤因子进行分析,实现了对塔机的损伤判定和损伤位置的确定。这种基于密度峰值聚类的诊断方法可对塔机微小损伤进行智能诊断和位置确定,该方法只需要塔机完好状态的数据和待检状态的数据即可自动诊断,解决了塔机损伤识别中损伤数据难以获取,因而无法实现智能训练和诊断的问题。

基于混合粒子群优化的交直流混联配电网扩展规划算法

在交直流混联配电网建设过程中,主要采用传统的粒子群算法求解配电网扩展规划方案,容易陷入局部最优,使得最终生成方案的规划成本较高。因此,提出基于混合粒子群优化的交直流混联配电网扩展规划算法。针对配电公司和分布式电源运营商两个配电网运营主体,设计配电公司为主、运营商为辅的博弈关系图,并构建以系统经济性最优、DG利用率最大为目标的双层配电网扩展规划模型。考虑交直流混联配电网运营要求,设置设备投资约束、网络连通性约束和元件运行特性约束条件。结合粒子群算法和模拟退火算法组成混合粒子群优化算法,应用对双层规划模型进行迭代求解,输出最优交直流混联配电网扩展规划方案。实验结果表明:所提算法应用后得出的配电网扩展方案年规划成本仅为135.07万元,满足了配电网扩展经济性要求。

双面渐进成形工艺参数优化及减薄率的预测

渐进成形的减薄率是衡量成形件质量的重要指标。文章采用Box-Behnken设计实验方案进行试验,分析了刀具直径D、层间距Δz、成形角α和板厚t对减薄率的影响,并得到试验最优的参数组合。建立了工艺参数到减薄率的BP神经网络模型,用数据训练集训练网络,计算测试集减薄率预测模型的精度。针对BP神经网络平均误差大(6.42%)的问题,用粒子群算法(PSO)优化了BP神经网络模型参数,使预测误差降低到2.24%。PSO-BP神经网络模型可以有效预测工艺参数和减薄率的关系。

基于改进粒子群算法的交直流混合配电网规划研究

随着电网电源和负荷快速增加,电网中的交直流混合将成为电网的一种新形式。因此,配电网规划更为复杂,故提出基于改进粒子群算法的交直流混合配电网规划研究。以成本最小化为目标和上层规划目标函数和下层规划目标函数。基于改进粒子群算法,以势阱处的能量为核心,确定粒子在势阱中运动最佳位置;保证上、下层参数传递收敛性,获取全局最优解,实现双层关联统一。利用不可行度约束下的支配函数,实现配电网规划解集多样性;计算相邻粒子拥挤距离,搜索配电网规划可行解,获取配电网规划结果。实验结果表明,研究的规划方法光伏数据比实际数据最大值高950 kW、风电数据比实际数据最大值低310 kW、负荷数据比实际数据最大值低95 kW,与理想规划效果一致。

双接地型转子吸振器的参数优化

利用智能优化算法对双接地型转子吸振器的参数进行了优化设计,探讨了质量比、附加位置对抑振性能的影响规律。首先,根据双接地型转子吸振器的结构构建了吸振器的力学模型。接着,利用有限元法建立了吸振器-转子系统的动力学方程。在此基础上,推导了转子的响应公式,并基于该公式建立了吸振器的参数优化数学模型。最后,利用改进的变异粒子群算法对吸振器的参数进行了优化,分析了质量比及附加位置对最优参数及最优振幅的影响规律。结果表明:在相同附加位置时,随着质量比的增加,吸振器的最优刚度kaopt和kgopt、最优阻尼cgopt及抑振率均呈递增规律,同时转子的最优振幅逐渐减小。当质量比为0.1时,吸振器的附加位置越靠近轴承,最优刚度kaopt和最优阻尼cgopt越小,抑振率越低,最优刚度kgopt受附加位置影响很小,趋于定值。

新型反射式双面光伏板对日跟踪装置设计

文章设计了一种基于新型光敏传感器和改进型PSO算法的双面光伏组件对日跟踪装置,该装置可从太阳能吸收最大化和功率输出最大化两个方面实现光电转化效率的提升。结合反射器和正、反面传感器暗盒采集的光敏信号,以STM32单片机及其外围电路为主控单元,采用低频唤醒和无线通信技术,可实现低功耗下双面光伏板的双精度双轴跟踪和信息采集;通过研究双面光伏板的电路模型和输出特性,采用改进型粒子群算法进行多峰值情况下最大功率点的追踪。试验证明,设计实现的装置光伏跟踪效果理想,双轴跟踪误差在1.59°以内,实现了快速、有效的最大功率输出。

Application of improved PSO to power transmission congestion management optimization model

The parameters of particles were encoded firstly, then the constraint conditions and fitness degree were processed, and the calculation steps of the improved PSO algorithm were presented. Finally, the issues with the adoption of the improved PSO algorithm were solved and the results were analyzed. The results show that it is beneficial to obtaining the optimal solution by increasing the number of particles but that will also increase the operation time. On the aspects of solving continuous differentiable non-linear optimization model with equality and inequality constraints, the optimization result of PSO algorithm is the same as that of the interior point method. Compared with genetic algorithms (GA), PSO algorithm is more effective in the local optimization, and unlike GA, it will not be early maturity. Meanwhile, PSO algorithm is also more effective in the boundary optimization than genetic algorithm.

基于IMPSO的双韦伯燃烧参数标定及预测

针对柴油机放热规律的拟合,提出了基于免疫粒子群优化(IMPSO)算法的双韦伯方程标定方法.对25%负荷和100%负荷的放热规律分别进行了标定,对双韦伯方程采用改进算法和免疫算法进行多解问题优化,优化结果表明:量纲为1的决定系数R2大于0.998,最优解稳定性可以达到0.700以上;对其他工况和机型放热规律进行标定,拟合和试验结果吻合,具有良好的泛化性,满足柴油机多工况的放热规律拟合要求;选择转速、循环喷油量、进气压力和进气温度作为输入,建立了多层前馈(BP)神经网络双韦伯方程预测模型,各参数R2均大于0.950,表明预测值与校准值吻合,验证了神经网络预测建模方法的可行性.

双档案粒子群算法求解柔性作业车间调度问题

针对最小化完工时间的柔性作业车间调度问题(FJSP),提出了双档案粒子群算法(DAPSO)。构建精英档案和进步档案分别存储具有较好适应值的精英粒子的个体历史最优位置(Pbest)和进步较快粒子的位置。利用精英档案中的粒子计算解可信度,并根据解可信度来判断当前群体所处的进化状态,粒子根据进化状态调整向两个档案中学习的力度以达到收敛性与多样性的平衡。在Kacem的5个测试问题和MK系列10个测试问题开展了广泛实验,通过与其他算法按照最小完工时间、平均完工时间进行了比较,证明了DAPSO在求解FJSP问题时的有效性。

局部遮阴下双优化PSO在光伏MPPT中的应用研究

局部阴影下,在光伏最大功率点追踪(maximum power point tracking,MPPT)过程中,以往的方法通常采用的是全局搜索,这一过程会出现搜索时间长、收敛速度慢、局部寻优、功率振荡时间长等问题。对此,提出双优化粒子群算法(particle swarm optimization,PSO),即先通过数学推导优化最大功率点存在的电压区间,由全局缩小范围,在此区间内再运用PSO搜索,并优化算法中的系数c_(1)、c_(2),引入搜索程度因子d来增强其收敛性。在MATLAB/Simulink仿真中设置两组光照并通过全局搜索算法中的传统PSO与改进PSO进行对比分析。仿真显示该方法在上述问题方面均有较为明显的改善。

双立柱堆垛机立柱挠度模型及其参数优化

针对当前双立柱堆垛机立柱顶端挠度计算模型精度不高、立柱优化算法效率低等问题,以XHF-B系列1-5T双立柱堆垛机为例,提出了一种新的立柱顶端挠度计算模型和立柱参数优化方法。首先,基于微元法的思想,建立了新的立柱顶端挠度计算模型;然后,对立柱进行了影响参数分析,建立了立柱挠度优化模型,并在此基础上,采用粒子群算法(PSO)对其进行了求解;最后,采用ANSYS Workbench软件,对优化后的双立柱堆垛机进行了有限元分析,并结合有限元分析结果,对理论模型的正确性进行了验证。研究结果表明:堆垛机立柱参数优化后,立柱顶端挠度为8.6199 mm,降低了74.8%,优化效果显著;有限元分析结果显示的立柱顶端挠度为9.1597 mm,与理论计算吻合度为94.1%,验证了立柱挠度优化模型的正确性,可为堆垛机设计提供理论依据。

计及风电场损耗的风电场无功优化分配策略研究

针对风电场有功损耗和节点电压易受无功功率影响,及利用当前信息进行控制而导致风机无功补偿不充分和无功越限的问题,提出了一种风电场无功优化控制策略。该策略首先进行风电功率预测,实时求解风电机组无功动态容量评估;然后,通过参考电压和实测电压求得风电场无功需求;最后,提出一种考虑风电场内有功损耗最小的风电机组无功分配方法,采用改进粒子群优化算法,对所构造的无功优化模型进行求解,实现对各个风机无功出力进行合理分配。通过模型对所提方法进行验证,结果表明风电场在满足无功需求指令的同时,能够减小系统的有功损耗,改善节点电压质量。

Adaptive Barebones Salp Swarm Algorithm with Quasi-oppositional Learning for Medical Diagnosis Systems: A Comprehensive Analysis

The Salp Swarm Algorithm(SSA)may have trouble in dropping into stagnation as a kind of swarm intelligence method.This paper developed an adaptive barebones salp swarm algorithm with quasi-oppositional-based learning to compensate for the above weakness called QBSSA.In the proposed QBSSA,an adaptive barebones strategy can help to reach both accurate convergence speed and high solution quality;quasi-oppositional-based learning can make the population away from traping into local optimal and expand the search space.To estimate the performance of the presented method,a series of tests are performed.Firstly,CEC 2017 benchmark test suit is used to test the ability to solve the high dimensional and multimodal problems;then,based on QBSSA,an improved Kernel Extreme Learning Machine(KELM)model,named QBSSA–KELM,is built to handle medical disease diagnosis problems.All the test results and discussions state clearly that the QBSSA is superior to and very competitive to all the compared algorithms on both convergence speed and solutions accuracy.

基于粒子群算法的虚拟场景双特征点匹配仿真

针对于多数虚拟场景图像的特征较为密集,且相近特征差异性较小,在匹配特征点时易出现误提取、误匹配情况,提出基于粒子群算法的双特征点匹配方法。利用粒子群算法在虚拟场景内遍历,得到虚拟图像各个特征点的属性信息。通过Moravec特征点算子提取方法对图像完成灰度化处理,基于最小灰度方差,选取局部区域内的最高兴趣点为特征点。利用三元二次函数去除虚拟图像中的不稳定极值点和无用点。计算特征点间的像元灰度协方差,选取阵列的中心点完成双特征点匹配。由仿真对比分析可知,所提方法能够实现双特征点的均匀匹配,有效解决了特征点扎堆匹配问题,且具有极低的误匹配率。

基于粒子群优化的双凸透镜缺陷Otsu阈值分割算法

为提高双凸透镜在实时缺陷检测时阈值分割的速度和精度,提出了改进粒子群优化算法(particle swarm optimization,PSO)优化Otsu双阈值分割(Otsu s thresholding method)。通过改进粒子群算法的权重函数并引入约束因子增强粒子前期全局搜索能力,提升了后期局部收敛速度;在判断是否陷入局部最优时加入扰动,防止粒子后期陷入局部最优;用粒子当前位置替换全局位置时,为减少粒子资源浪费,通过重新分配位置和速度激活粒子搜索能力,提升了整体粒子群的全局搜索能力。实验证明,采用改进的PSO算法对图像进行双阈值分割,比Otsu双阈值分割节省约52.7%的时间,比PSO+Otsu算法节省约32.3%的时间,而且其阈值分割的精度也得到了提升。