Vibration Suppression for Active Magnetic Bearings Using Adaptive Filter with Iterative Search Algorithm

Active Magnetic Bearing(AMB) is a kind of electromagnetic support that makes the rotor movement frictionless and can suppress rotor vibration by controlling the magnetic force. The most common approach to restrain the rotor vibration in AMBs is to adopt a notch filter or adaptive filter in the AMB controller. However, these methods cannot obtain the precise amplitude and phase of the compensation current. Thus, they are not so effective in terms of suppressing the vibrations of the fundamental and other harmonic orders over the whole speed range. To improve the vibration suppression performance of AMBs,an adaptive filter based on Least Mean Square(LMS) is applied to extract the vibration signals from the rotor displacement signal. An Iterative Search Algorithm(ISA) is proposed in this paper to obtain the corresponding relationship between the compensation current and vibration signals. The ISA is responsible for searching the compensating amplitude and shifting phase online for the LMS filter, enabling the AMB controller to generate the corresponding compensation force for vibration suppression. The results of ISA are recorded to suppress vibration using the Look-Up Table(LUT) in variable speed range. Comprehensive simulations and experimental validations are carried out in fixed and variable speed range, and the results demonstrate that by employing the ISA, vibrations of the fundamental and other harmonic orders are suppressed effectively.

Area-based non-maximum suppression algorithm for multi-object fault detection

Unmanned aerial vehicle(UAV)photography has become the main power system inspection method;however,automated fault detection remains a major challenge.Conventional algorithms encounter difficulty in processing all the detected objects in the power transmission lines simultaneously.The object detection method involving deep learning provides a new method for fault detection.However,the traditional non-maximum suppression(NMS)algorithm fails to delete redundant annotations when dealing with objects having two labels such as insulators and dampers.In this study,we propose an area-based non-maximum suppression(A-NMS)algorithm to solve the problem of one object having multiple labels.The A-NMS algorithm is used in the fusion stage of cropping detection to detect small objects.Experiments prove that A-NMS and cropping detection achieve a mean average precision and recall of 88.58%and 91.23%,respectively,in case of the aerial image datasets and realize multi-object fault detection in aerial images.

Optimization Design of Fairings for VIV Suppression Based on Data-Driven Models and Genetic Algorithm

Vortex induced vibration(VIV)is a challenge in ocean engineering.Several devices including fairings have been designed to suppress VIV.However,how to optimize the design of suppression devices is still a problem to be solved.In this paper,an optimization design methodology is presented based on data-driven models and genetic algorithm(GA).Data-driven models are introduced to substitute complex physics-based equations.GA is used to rapidly search for the optimal suppression device from all possible solutions.Taking fairings as example,VIV response database for different fairings is established based on parameterized models in which model sections of fairings are controlled by several control points and Bezier curves.Then a data-driven model,which can predict the VIV response of fairings with different sections accurately and efficiently,is trained through BP neural network.Finally,a comprehensive optimization method and process is proposed based on GA and the data-driven model.The proposed method is demonstrated by its application to a case.It turns out that the proposed method can perform the optimization design of fairings effectively.VIV can be reduced obviously through the optimization design.

Novel Retinex algorithm by interpolation and adaptive noise suppression

In order to improve image quality,a novel Retinex algorithm for image enhancement was presented.Different from conventional algorithms,it was based on certain defined points containing the illumination information in the intensity image to estimate the illumination.After locating the points,the whole illumination image was computed by an interpolation technique.When attempting to recover the reflectance image,an adaptive method which can be considered as an optimization problem was employed to suppress noise in dark environments and keep details in other areas.For color images,it was taken in the band of each channel separately.Experimental results demonstrate that the proposed algorithm is superior to the traditional Retinex algorithms in image

A Fixed Suppressed Rate Selection Method for Suppressed Fuzzy C-Means Clustering Algorithm

Suppressed fuzzy c-means (S-FCM) clustering algorithm with the intention of combining the higher speed of hard c-means clustering algorithm and the better classification performance of fuzzy c-means clustering algorithm had been studied by many researchers and applied in many fields. In the algorithm, how to select the suppressed rate is a key step. In this paper, we give a method to select the fixed suppressed rate by the structure of the data itself. The experimental results show that the proposed method is a suitable way to select the suppressed rate in suppressed fuzzy c-means clustering algorithm.

Improved DSE-CMA Error Function for Blind Multiuser Interference Suppression in DS/CDMA Systems

This paper presents an improved error function of dithered signed-error constant modulus algorithm （IDSE-CMA） for blind multiuser interference suppression in DS/CDMA systems. It uses a different error function to replace the former one in sign operation of the DSE-CMA and compares their performance in multiple access interference （MAI） suppression ability. Simulations indicate that the new algorithm has better performance than the similar CMA in terms of convergence speed and steady-state performance：

Radio Disturbance Suppression for Ground Wave Radar

A new method of single sample polarization filtering is proposed. The algorithm is fast and suitable for the polarization processing of stationary or nonstationary polarized disturbed signals with one or more independent disturbances. A ground wave polarimetric radar with the ability of radio disturbance suppression is then introduced. Some numerical results demonstrate the effectiveness of single sample polarization filtering method for ground wave polarimetric radar.

A Designated Harmonic Suppression Technology for Sampled SPWM

Sampled SPWM is an excellent VVVF method of motor speed control, meanwhile the harmonic components of the output wave impairs its applications in practice. A designated harmonic suppression technology is presented for sampled SPWM, which is an improved algorithm for the harmonic suppression in high voltage and high frequency spectrum. As the technology is applied in whole speed adjusting range, the voltage can be conveniently controlled and high frequency harmonic of SP WM is also improved.

抑烟沥青复掺配比优化及抑烟效果评价

为抑制沥青在高温下烟气的产生,降低环境污染及对作业人员健康的影响程度,本文进行了抑烟沥青复掺配比优化研究。基于单掺抑烟沥青的针入度、延度及软化点及自制沥青烟生成富集装置的沥青产烟结果,结合熵权法与遗传算法进行抑烟沥青复掺配比优化,并分析优化复掺抑烟剂在沥青中的分散均匀性、储存稳定性及其对沥青特征官能团的影响;基于优化复掺配比的抑烟沥青高、中、低温性能测试,并对其抑烟效果进行评价,验证优化复掺配比方法的有效性。研究表明:抑烟剂均匀分散于沥青中,且未对沥青特征官能团产生改变,优化复掺抑烟沥青满足储存稳定性要求。优化复掺抑烟剂对沥青高、低温性能具有改善作用,并提升了沥青中温抗疲劳性能。沥青烟收集前后滤管颜色变化表明该沥青烟富集方式具有较高的可靠性,且优化复掺抑烟沥青具有显著抑烟效果,最大抑烟率为99.7%。优化复掺配比下的指标预测值与实测值差异较小,证明了该优化复掺配比方法的可靠性。

基于Adaline算法的高速磁浮列车谐波检测及抑制方法

[目的]为了减小高速磁浮谐波对牵引网产生的影响,保障高速磁浮列车的正常运行,需研究高速磁浮牵引供电系统中的谐波特性及其相应的谐波抑制方法。[方法]分析了高速磁浮牵引供电系统的构成,并对双三相半控12脉冲多重整流电路及其谐波进行了理论分析和仿真。根据Adaline神经网络算法原理和高速磁浮谐波特性,构建基于Adaline的高速磁浮谐波检测模型。采用谐波补偿控制策略,产生与检测出的谐波方向相反、大小相等的电流作为补偿信号,对系统畸变电流进行谐波抑制。[结果及结论]仿真结果表明:采用基于Adaline算法的谐波检测及抑制方法前,谐波畸变率为11.16%,而采用该方法后,各次谐波含量明显降低,谐波畸变率下降为1.41%。研究结果表明:所提谐波检测及抑制方法可以有效减少谐波畸变,提高牵引供电系统的稳定性和工作效率。

基于改进SFLA-Elman神经网络的电离层杂波抑制方法

针对高频地波雷达目标检测中电离层杂波的干扰问题,提出了一种基于改进混合蛙跳算法优化Elman神经网络预测抑制电离层杂波的策略。为解决混合蛙跳算法初始种群分布不均匀、收敛精度低、易陷于局部极值等问题,引入Cubic混沌映射、莱维飞行策略、非线性平衡因子和复制操作,增强种群多样性,提高算法搜索能力。利用改进后的算法和其他算法分别优化Elman神经网络预测抑制模型,结果表明,改进后的算法无论是在收敛精度和稳定性上,还是在临近距离单元电离层杂波的预测抑制上,都取得了显著的提升。在基本保留目标信号的基础上,平均信杂比较原始回波提升18.52 dB,较原始混合蛙跳算法提升1.08 dB,对于电离层杂波的抑制具有较高应用价值。

光伏波动平抑下改进K-means的电池储能动态分组控制策略

针对电池储能系统(battery energy storage system,BESS)进行光伏波动平抑时寿命损耗高及荷电状态(state of charge,SOC)一致性差的问题,提出了光伏波动平抑下改进K-means的BESS动态分组控制策略。首先,采用最小最大调度方法获取光伏并网指令。其次,设计了改进侏儒猫鼬优化算法(improved dwarf mongoose optimizer,IDMO),并利用它对传统K-means聚类算法进行改进,加快了聚类速度。接着,制定了电池单元动态分组原则,并根据电池单元SOC利用改进K-means将其分为3个电池组。然后,设计了基于充放电函数的电池单元SOC一致性功率分配方法,并据此提出BESS双层功率分配策略,上层确定电池组充放电顺序及指令,下层计算电池单元充放电指令。对所提策略进行仿真验证,结果表明,所设计的IDMO具有更高的寻优精度及更快的寻优速度。所提BESS平抑光伏波动策略在有效平抑波动的同时,降低了BESS运行寿命损耗并提高了电池单元SOC的均衡性。

应用模糊PID自修正的电镀槽液温度控制方法

电镀槽液温度自动控制过程属于典型的混沌过程,在温度采集的过程中存在明显的非线性波动。典型的比例-积分-微分(PID)控制算法在这种混沌特性与非线性特性干扰下,存在控制精度较低、稳定性和鲁棒性较差等问题。此外,为解决非线性问题,PID算法需要循环迭代计算,存在控制时滞问题。以模糊控制为基础,提出了基于模糊PID的电镀槽液温度自动控制方法。该方法先设计温度数据采集结构,并将移动平均滤波、温度修正算法引入到采用的数据采集结构中,完成电镀槽液的实时温度数据采集。然后将采集到的温度数据作为构建的模糊PID控制器的输入。利用模糊控制规则引入修正因子,实现电镀槽液温度的自动控制。考虑到电镀槽液的时滞特性,在控制器中加入粗糙预估参考模型,对该特性加以抑制,提升控制性能。最后应用实验证明了所提方法的先进性。实验结果表明:所提方法具有较高的控制精度和效率,能够有效降低超调量和波动,应用效果较好。

节流槽结构参数对阀开启压力冲击及空化特性的影响研究

液压控制阀阀芯节流槽的结构对阀的动态特性影响强烈,不良节流槽结构将导致极大的液压阀开启瞬间压力冲击。针对某型号负载敏感多路阀在甘蔗联合收割机的应用中出现啸叫和液压冲击的问题,结合动网格与RNG-k-ε湍流模型,对U+T型节流槽和V型节流槽的压力冲击及空化进行了对比分析,发现同等条件下V型槽的阀芯较U+T型槽的阀芯的压力冲击下降了19.37%,最大空化程度降低了43.7%。选取V型节流槽开展进一步研究,通过正交试验获取不同节流槽结构参数下的压力峰值;采用BP神经网络建立V型节流槽结构参数与压力冲击的代理模型,结合灰狼优化(GWO)算法得到节流槽结构参数的最优配置;依据优化结果制作阀芯并进行试验,结果显示,抑制压力冲击效果显著,系统压力超调下降至原来52.4%。

一种基于动态门限与LMS算法相结合的多径干扰抑制算法

在船舰行驶过程中,信号的传输在岛屿反射与海面散射影响下易产生多径效应,使船舰的无线地空数据接收系统受到影响。为提高接收机的接收性能,首先完成了多径信道的建模,搭建了三径信道模型,并在此模型下,将动态门限法与最小均方(LMS)算法进行改进结合,并用此算法完成了信号处理的研究与仿真。仿真结果表明,所提算法优化方案可以提高在此种情况下信号接收系统的准确性。

一种共址平台装备的射频干扰抑制系统设计方法

在共址平台场景下,大功率发射装备工作时辐射的信号会对其附近的高灵敏度接收机造成严重干扰。针对这一问题,采用一种多抽头射频干扰对消系统,通过控制系统中的时延、幅度和衰减的权值,使得产生的参考信号与干扰信号等幅反向,从而实现干扰信号的对消。为了获得最优的干扰对消性能,提出了一种基于优化算法获取多抽头最优控制权值的方法。与传统获取控制权值的方法相比,所提方法具有抽头支路少、干扰抑制性能高和对数字控制电路要求低等优点。

基于无监督神经网络匹配算法的叠前表面多次波压制方法

为了有效地压制海上地震勘探数据中的表面多次波,实现勘探目标的正确成像,提出一种基于无监督神经网络的叠前表面多次波匹配算法,将神经网络方法与表面相关多次波压制方法相结合,通过设定学习率不断下降,用无监督神经网络取代匹配滤波算子,对叠前地震数据进行表面多次波的压制,既不需要传统的匹配算法,也不需要在标签数据集上进行训练。在简单合成数据、Sigsbee模型数据和实际数据上的应用结果验证了该方法对表面多次波压制的有效性。

基于惯性运动自适应前馈的磁轴承基座扰动抑制

为抑制主动磁轴承中基座的运动造成转子的振动增大,设计并实现了一种基于惯性运动的前馈控制方法。首先,针对基座运动状态下的磁轴承系统,建立了基座运动的五自由度磁轴承转子动力学模型,然后,分析了在基座进行小幅度复杂运动时转子受扰动力的动力学,提出了一种基于自适应算法的惯性前馈方法。最后,采用搭建的实验平台对不同扰动下开启前馈控制前后转子的响应进行了实验研究。实验结果表明:自适应前馈控制器使能后,磁轴承转子的位移减少了80%以上。设计的自适应前馈控制方法能够有效抑制磁悬浮系统基座扰动对转子振动的影响,大幅提高了磁轴承转子的运行精度,且设计的系统仅需增加一个小体积的惯性微机电测量单元,满足实际工程应用需求。

机械弹性储能用永磁同步电机多源谐波提取及统一抑制

针对柔性涡簧振动和电力电子装置等引起机械弹性储能(MEES)系统机械侧谐波难以准确建模、多源谐波无法整体表征及统一抑制的问题,该文提出MEES用永磁同步电机多源谐波提取及统一抑制方法。首先,建立MEES系统数学模型,利用电机转速谐波对多源谐波进行整体表征;然后,提出融合奇异值差分的矩阵束算法,利用其对含噪实测转速数据进行处理以获取包含不同频率成分的多源谐波信息;最后,设计定子电流矢量定向下基于反推控制的多源谐波统一抑制算法,完成对不同频率成分谐波的同时抑制。仿真和实验结果表明,利用融合奇异值差分的矩阵束算法能快速、准确地识别转速信号中的多源谐波成分,统一抑制算法有效降低了多源谐波导致的电磁转矩脉动,改善了系统运行性能。