Application of neural networks for permanent magnet synchronous motor direct torque control

Neural networks require a lot of training to understand the model of a plant or a process. Issues such as learning speed, stability, and weight convergence remain as areas of research and comparison of many training algorithms. The application of neural networks to control interior permanent magnet synchronous motor using direct torque control （DTC） is discussed. A neural network is used to emulate the state selector of the DTC. The neural networks used are the back-propagation and radial basis function. To reduce the training patterns and increase the execution speed of the training process, the inputs of switching table are converted to digital signals, i.e., one bit represent the flux error, one bit the torque error, and three bits the region of stator flux. Computer simulations of the motor and neural-network system using the two approaches are presented and compared. Discussions about the back-propagation and radial basis function as the most promising training techniques are presented, giving its advantages and disadvantages. The system using back-propagation and radial basis function networks controller has quick parallel speed and high torque response.

Analysis of Permanent Magnet-assisted Synchronous Reluctance Motor Based on Equivalent Reluctance Network Model

In this paper,the equivalent reluctance network model(ERNM)is used to calculate the magnetic circuit of a permanent magnet-assisted synchronous reluctance motor(PMASynRM)and calculate no-load air-gap magnetic field and electromagnetic torque.Iteration method is used to solve the relative permeability of iron core.A novel reluctance network model based on actual distribution of the magnetic flux inside the motor is established.The magnetomotive force(MMF)generated by armature winding affects the relative permeability of iron core,which is considered in the calculation of ERNM to improve the accuracy when the motor is under load.ERNM can be used to measure air-gap flux density,no-load back electromotive force(EMF),the average value of motor torque,the armature winding voltage under load,and power factor.The method of calculating the motor performance is proposed.The results of calculation are consistent with finite element method(FEM)and the computational complexity is much less than that of the FEM.The results of ERNM has been verified,which will provide a simple method for motor design and analysis.

Neural network-based model for prediction of permanent deformation of unbound granular materials

Several available mechanistic-empirical pavement design methods fail to include predictive model for permanent deformation(PD)of unbound granular materials(UGMs),which make these methods more conservative.In addition,there are limited regression models capable of predicting the PD under multistress levels,and these models have regression limitations and generally fail to cover the complexity of UGM behaviour.Recent researches are focused on using new methods of computational intelligence systems to address the problems,such as artificial neural network(ANN).In this context,we aim to develop an artificial neural model to predict the PD of UGMs exposed to repeated loads.Extensive repeated load triaxial tests(RLTTs)were conducted on base and subbase materials locally available in Victoria,Australia to investigate the PD properties of the tested materials and to prepare the database of the neural networks.Specimens were prepared over different moisture contents and gradations to cover a wide testing matrix.The ANN model consists of one input layer with five neurons,one hidden layer with twelve neurons,and one output layer with one neuron.The five inputs were the number of load cycles,deviatoric stress,moisture content,coefficient of uniformity,and coefficient of curvature.The sensitivity analysis showed that the most important indicator that impacts PD is the number of load cycles with influence factor of 41%.It shows that the ANN method is rapid and efficient to predict the PD,which could be implemented in the Austroads pavement design method.

Analysis of temperature field for a surface-mounted and interior permanent magnet synchronous motor adopting magnetic-thermal coupling method

Aiming at obtaining high power density of surface-mounted and interior permanent magnet synchronous motor(SIPMSM),it is important to accurately calculate the temperature field distribution of SIPMSM,and a magnetic-thermal coupling method is proposed.The magnetic-thermal coupling mechanism is analyzed.The thermal network model and finite element model are built by this method,respectively.The effects of power frequency on iron losses and temperature fields are analyzed by the magnetic-thermal coupling finite element model under the condition of rated load,and the relationship between the load and temperature field is researched under the condition of the synchronous speed.In addition,the equivalent thermal network model is used to verify the magnetic-thermal coupling method.Then the temperatures of various nodes are obtained.The results show that there are advantages in both computational efficiency and accuracy for the proposed coupling method,which can be applied to other permanent magnet motors with complex structures.

Design Optimization of Permanent Magnet Eddy Current Coupler Based on an Intelligence Algorithm

The permanent magnet eddy current coupler(PMEC)solves the problem of flexible connection and speed regulation between the motor and the load and is widely used in electrical transmission systems.It provides torque to the load and generates heat and losses,reducing its energy transfer efficiency.This issue has become an obstacle for PMEC to develop toward a higher power.This paper aims to improve the overall performance of PMEC through multi-objective optimization methods.Firstly,a PMEC modeling method based on the Levenberg-Marquardt back propagation(LMBP)neural network is proposed,aiming at the characteristics of the complex input-output relationship and the strong nonlinearity of PMEC.Then,a novel competition mechanism-based multi-objective particle swarm optimization algorithm(NCMOPSO)is proposed to find the optimal structural parameters of PMEC.Chaotic search and mutation strategies are used to improve the original algorithm,which improves the shortcomings of multi-objective particle swarm optimization(MOPSO),which is too fast to converge into a global optimum,and balances the convergence and diversity of the algorithm.In order to verify the superiority and applicability of the proposed algorithm,it is compared with several popular multi-objective optimization algorithms.Applying them to the optimization model of PMEC,the results show that the proposed algorithm has better comprehensive performance.Finally,a finite element simulation model is established using the optimal structural parameters obtained by the proposed algorithm to verify the optimization results.Compared with the prototype,the optimized PMEC has reduced eddy current losses by 1.7812 kW,increased output torque by 658.5 N·m,and decreased costs by 13%,improving energy transfer efficiency.

Design of Rotor Magnetic Barrier Structure of Built-in Permanent Magnet Motor Based on Taguchi Method

In this paper,a 20kW vehicle built-in permanent magnet synchronous motor is taken as an example,and a magnetic barrier structure is added to the rotor of the motor to solve the uneven saturation problem of the rotor side magnetic bridge.This structure improves the air-gap flux density waveform of the motor by influencing the internal magnetic flux path of the motor rotor,thus improving the sine of the no-load back EMF waveform of the motor and reducing the torque ripple of the motor.At the same time,Taguchi method is used to optimize the structural parameters of the added magnetic barrier.In order to facilitate the analysis of its uneven saturation phenomenon and improve the optimization effect,a simple equivalent magnetic network(EMN)model considering the uneven saturation of rotor magnetic bridge is established in this paper,and the initial values of optimization factors are selected based on this model.Finally,the no-load back EMF waveform distortion rate,torque ripple and output torque of the optimized motor are compared and analyzed,and the influence of magnetic barrier structure parameters on the electromagnetic performance of the motor is also analyzed.The results show that the optimized motor can not change the output torque of the motor as much as possible on the basis of reducing the waveform distortion rate of no-load back EMF and torque ripple.

Chaos in complex motor networks induced by Newman-Watts small-world connections

We investigate how dynamical behaviours of complex motor networks depend on the Newman-Watts small-world （NWSW） connections. Network elements are described by the permanent magnet synchronous motor （PMSM） with the values of parameters at which each individual PMSM is stable. It is found that with the increase of connection probability p, the motor in networks becomes periodic and falls into chaotic motion as p further increases. These phenomena imply that NWSW connections can induce and enhance chaos in motor networks. The possible mechanism behind the action of NWSW connections is addressed based on stability theory.

A Novel Approach for Fault Diagnosis in Wireless Sensor Networks

The following article has been retracted due to special reason of the author. This paper published in Vol.5 No. 2, 2013, has been removed from this site.

A Novel Operational Partition between Neural Network Classifiers on Vulnerability to Data Mining Bias

It is difficult if not impossible to appropriately and effectively select from among the vast pool of existing neural network machine learning predictive models for industrial incorporation or academic research exploration and enhancement. When all models outperform all the others under disparate circumstances, none of the models do. Selecting the ideal model becomes a matter of ill-supported opinion ungrounded on the extant real world environment. This paper proposes a novel grouping of the model pool grounded along a non-stationary real world data line into two groups: Permanent Data Learning and Reversible Data Learning. This paper further proposes a novel approach towards qualitatively and quantitatively demonstrating their significant differences based on how they alternatively approach dynamic and raw real world data vs static and prescient data mining biased laboratory data. The results across 2040 separate simulation runs using 15,600 data points in realistically operationally controlled data environments show that the two-group division is effective and significant with clear qualitative, quantitative and theoretical support. Results across the empirical and theoretical spectrum are internally and externally consistent yet demonstrative of why and how this result is non-obvious.

A regional GNSS-VTEC model over Nigeria using neural networks： A novel approach

A neural network model of the Global Navigation Satellite System - vertical total electron content （GNSS-VTEC） over Nigeria is developed. A new approach that has been utilized in this work is the consideration of the International Reference Ionosphere＇s （IRI＇s） critical plasma frequency （foF2） parameter as an additional neuron for the network＇s input layer. The work also explores the effects of using various other input layer neurons like distur- bance storm time （DST） and sunspot number. All available GNSS data from the Nigerian Permanent GNSS Network （NIGNET） were used, and these cover the period from 2011 to 2015, for 14 stations. Asides increasing the learning accuracy of the networks, the inclusion of the IRI＇s foF2 parameter as an input neuron is ideal for making the networks to learn long-term solar cycle variations. This is important especially for regions, like in this work, where the GNSS data is available for less than the period of a solar cycle. The neural network model developed in this work has been tested for time-varying and spatial per- formances. The latest 10% of the GNSS observations from each of the stations were used to test the forecasting ability of the networks, while data from 2 of the stations were entirely used for spatial performance testing. The results show that root-mean-squared-errors were generally less than 8.5 TEC units for all modes of testing performed using the optimal network. When compared to other models, the model developed in this work was observed to reduce the prediction errors to about half those of the NeQuick and the IRI model.

Lumped-Parameter Thermal Network Model and Experimental Research of Interior PMSM for Electric Vehicle

A 25kW interior permanent magnet synchronous machine(IPMSM)applied to the electric vehicle is introduced in the paper.A lumped-parameter thermal network model is presented for IPMSM temperature rise calculation.Furthermore,a 3D liquid-solid coupling model considering the assembly clearance is compared with the 2D lumped-parameter thermal network model.Finally,a dynamometer platform for temperature rise measurement is established to verify the above-mentioned methods,which obtains the measured efficiency map at rated load case and overload case.At the same time,the measured no-load back electromotive Force(EMF),load line input voltage and load current are gathered.Thermocouple PTC100 is used to measure the temperature of the stator winding and iron core,and the FLUKE infrared thermal imager is applied to measure the surface temperature of PMSM and controller.Testing result shows that the lumped-parameter thermal network have a high accuracy to predict each part temperature.

YASA轴向磁通永磁电机定子槽漏感计算

定子槽漏感是定子无轭模块化(yokeless and segmented,YASA)轴向磁通永磁电机电感的主要分量,会降低电机的功率因数和最大输出转矩。该文提出定子槽内微小单元磁场能量法计算YASA电机的定子槽漏感,通过建立计及铁心饱和影响的二维等效磁网络模型准确计算槽内微小单元储存的磁场能量,进而计算定子槽漏感。基于所提出的方法,进一步研究定子结构参数以及极槽配合对YASA电机定子槽漏感的影响规律。有限元仿真和实验结果表明,该文所提出的YASA电机定子槽漏感计算方法的可行性和准确性。

航空电推进电机多层波浪形拓扑及散热设计方法

针对航空电推进电机的散热问题,提出一种基于多层波浪形散热拓扑的航空电推进电机高效散热设计方法。航空电推进电机采用多层波浪形散热拓扑,建立电机等效热网络模型,确定等效热阻、对流换热系数等重要参数,完成电机温度精确计算,并通过CFD仿真验证所建立电机等效热网络模型的准确性和有效性。以此为基础,对比分析传统散热翅和多层波浪形散热拓扑对电机功率密度的影响。基于多层波浪形散热拓扑的等效热网络模型,采用遗传学习粒子群优化(GL-PSO)算法,完成航空电推进电机高效散热优化设计。优化结果表明:相比于原始方案,优化方案的机壳质量减轻15.1%,整个电机的功率密度提升0.06 kW/kg。

基于残差卷积网络的多传感器融合永磁同步电机故障诊断

作为工业生产与日常生活的常见设备,永磁同步电机的故障诊断研究具有十分重要的意义。以永磁同步电机的匝间短路、退磁、轴承故障为诊断目标,提出一种新型的多传感器特征融合网络(MSFFN),结合多传感器融合技术与卷积神经网络实现永磁同步电机的可靠故障诊断。网络采用2个带有残差模块的卷积神经网络,对输入的电流信号与振动信号并行提取隐藏特征,并设计一种中间特征融合模块(IFFM)有效融合电流和振动的各层隐藏特征,IFFM基于注意力机制对网络中的电流特征与振动特征进行筛选,自适应关注不同信号的内在相关特征,以实现更好的诊断效果。搭建了故障样机测试平台进行数据采集与实验验证,实验结果表明,提出方法具有更高的诊断准确率,同时在叠加了强噪声的条件下,具备更强的抗干扰能力。

艰险山区铁路建造期通信综合承载网研究

艰险山区铁路沿线的移动通信基础设施大多较薄弱。在施工建造期,铁路沿线具有语音、数据、视频监控和应急通信等业务需求。分析梳理艰险山区铁路沿线基础设施监测业务,对建造期语音、数据和视频监控需求带宽进行调研,针对施工现场不同的通信网络覆盖场景和不同的业务承载需求,提出具有针对性的施工通信解决方案,采用网络融合方式,搭建一套覆盖全线的建造期通信综合承载平台,实现基础设施监测、语音、视频和应急等业务的传输。同时,探讨建造期通信承载网在运营期开展永临结合,提出永临结合的建设思路。

基于神经网络的永磁同步电机模型预测电流控制

针对备选电压矢量有限导致永磁同步电机有限集模型预测电流控制性能较差及计算量较大的问题,提出基于神经网络的永磁同步电机模型预测电流控制。基于7个基本电压矢量和121个扩展电压矢量的永磁同步电机模型预测电流控制分别建立7分类和121分类神经网络。随着备选电压矢量的增加,模型预测电流控制性能提升,对应的神经网络控制性能也得到改善,但分类任务数也随之增加。对于多步模型预测控制,计算量随步长呈指数上升,但输出电压矢量不变。因此,基于两步模型预测电流控制建立7分类神经网络。仿真结果表明:以上神经网络控制均可行,性能与相对应的模型预测电流控制基本相当。实时性实验结果表明相较于单步模型预测电流控制,神经网络控制并不占优势,但相较于两步模型预测电流控制,神经网络实时性有明显优势,计算耗时减小29.58%,表明神经网络控制更适于多步模型预测电流控制。

复杂工况下的永磁同步电机典型绕组故障在线诊断

绕组故障作为永磁同步电机常见的故障之一,严重影响电机正常运行。但由于电机运行工况复杂,故障特征波动严重,基于故障特征的诊断精度较低。为提高复杂工况下绕组故障的诊断精度,该文提出一种复杂工况下基于控制器信号的在线诊断方法。首先,对典型绕组故障进行故障机理分析,并通过基于自适应随机窗的快速傅里叶变换(FFT),提取控制器信号的相应故障特征;其次,通过研究单一工况和复杂工况下的各故障特征分布,揭示部分故障特征会在低转速工况下失效;再次,定义了复杂工况下故障特征性能指标,用于筛选故障特征;最后,在人工神经网络的基础上,提出了深度优化人工神经网络,引入批量归一化(BN)算法,并对深度网络结构残差化,提高网络泛化能力和诊断准确性。实验结果表明,通过计算故障特征性能指标,能够在诊断前对故障特征进行有效筛选,且深度优化人工神经网络的诊断准确性高、泛化能力强,在复杂工况下能够实现电机典型绕组故障的精确在线诊断。

基于小样本迁移代理模型的高速永磁电机应力优化

针对目前电机代理模型样本空间计算效率低的问题,该文提出迁移径向基函数神经网络代理模型。该模型的样本信息源于足量可快速获取或先验积累的低可信度数据和少量高可信度的数据。基于高速永磁电机应力优化问题,研究该模型对小样本的学习能力、对电机相似拓扑结构的学习能力以及基函数对迁移模型精度的影响,证明该模型在工程优化中的效率优势。最后展望迁移学习技术在电机优化中的发展趋势。

基于参数辨识的永磁同步直线电机循环神经网络多维观测器

该文研究了一种基于智能在线扩展卡尔曼滤波的永磁同步直线电机高精度循环神经网络多维观测器。首先,为了提高观测器精度,建立了两相旋转坐标系下带有互感和时延扰动的直线电机数学模型;其次,基于此模型设计了循环神经网络多维观测器,并实现了磁链和速度的高精度在线观测;然后,针对系统参数时变的问题,提出了一种智能在线扩展卡尔曼滤波多参数辨识算法,提高了参数辨识的精准度;最后,搭建基于MT1050的半实物永磁同步直线电机控制平台,实验结果验证了所提观测器的准确性和高效性。

基于重合相有功功率的配电网永久性故障识别方法

针对盲目重合于永久性故障会给配电网带来二次冲击等问题,提出一种基于重合相有功功率的配电网永久性故障识别方法。首先,建立了配电网分相重合时的分析模型,推导了不同中性点接地方式下瞬时性和永久性故障时的重合相有功功率表达式,得到了不同故障性质下有功功率的差异;其次,构建了基于有功功率的永久性故障识别判据,分析了过渡电阻、负荷大小等因素的影响,得到了所提方法在不同中性点接地方式系统中的适用范围并提出了分相重合策略;最后,通过PSCAD/EMTDC仿真结果验证了理论分析正确,以及所提永久故障识别方法耐过渡电阻能力较强。