An artificial neural network based adaptive power system stabilizer

An artificial neural network (ANN)-based power system stabilizer (PSS) and its application to power systems are presented. The ANN-based PSS combines the advantages of self-optimizing pole shifting adaptive control strategy and the quick response of ANN to introduce a new generation PSS. A popular type of ANN, the multilayer perceptron with error backpropagation training method, is used in this PSS. The ANN was trained by the training data group generated by the adaptive power system stabilizer (APSS). During the training, the ANN was required to memorize and simulate the control strategy of APSS until the differences were within the specified criteria. Results show that the proposed ANN-based PSS can provide good damping of the power system over a wide operating range and significantly improve the dynamic performance of the system     

Core loss in buried magnet permanent magnet synchronous motors

The steady-state core-loss characteristics of buried-magnet synchronous motors operating from a sinusoidal constant frequency voltage supply are investigated. Measured and calculated core loss, with constant shaft load, is shown to increase with decreasing terminal voltage due to an increase in armature reaction-induced stator flux-density time harmonics. Finite-element modeling is used to show that the additional loss due to the time-harmonic fields can increase core loss by a factor of six over the loss associated with only the fundamental component field at low motor flux levels. A simple air-gap model of motor flux components shows that this increased loss is due to localized rotor saturation. Thus, stator-core harmonic fields should be expected for all buried-magnet rotor synchronous motors (with or without a cage) operating at low flux levels. This factor becomes increasingly important when the motors are operated in the high-speed low-flux mode in conjunction with a variable-speed drive     

永磁同步风电机组神经网络滑模多目标优化控制

将永磁同步风力发电机组中的变流器和电网用等效负载代替并对控制回路进行简化,得到非线性仿射形式的机组模型,利用反馈线性化方法对系统进行精确线性化。固定参数离散指数趋近律滑模控制算法主要缺陷是如两个参数匹配不当,可能会使求得的控制量过大,同时系统在滑模面附近剧烈的高频抖振会导致机组所要承受的机械应力增加,动态性能变差,利用神经网络的自适应学习能力对这两个控制参数进行实时优化,根据机组控制目标定义一个综合性能指标,通过优化该指标得到网络权值修正算法。仿真结果表明,该方法可以使系统快速到达滑模面,实现了机组对最优转速的快速跟踪;同时有效抑制了系统的抖振,减小了额外的疲劳载荷,实现了多目标优化控制。     

Field-oriented vector control of synchronous motors with additional field winding

This paper presents a new method to control a synchronous motor in such a way to resemble the characteristics of a DC motor. The method suggests including a second field winding to the rotor of a voltage-source-inverter-fed synchronous motor. The angular frequency of the inverter is made equal to the angular rotor speed, (of a self-controlled synchronous motor drive). The added field winding is in space quadrature to the main field winding and is properly excited in such a way as to diminish the direct axis component of the stator current at every load conditions. The motor is controlled to operate with zero power angle from the inverter side and zero direct axis current from the rotor excitation side. Therefore, it operates with minimum stator current and with unity power factor. The addition of the second field winding will not complicate the design because it is just a control winding. This winding may be made with smaller wire cross-section and a larger number of turns. The control on this winding is not complicated and it can be easily created. The synchronous motor along with the added field and the required control loops are simulated and tested extensively. The test results show excellent motor performance in motoring and regenerating modes of operation.     

Application of artificial neural network to estimate power generation and efficiency of a new axial flux permanent magnet synchronous generator

An estimation study on the output power and the efficiency of a new-designed axial flux permanent magnet synchronous generator (AFPMSG) is performed. For the estimation algorithm, a multi-layer feedforward artificial neural network (ANN) is developed. Various experimental results from the generator have been used for the training purpose in the cases of different electrical loads and rotational speeds. Some experimental data is kept out of the training process for testing the network and the errors have been evaluated after the formation of the network. According to the findings, a network with three layers has been adequate to achieve very good error percentage between the ANN and laboratory studies. The maximal testing error percentages are found to be nearly 3% and 4% for the output power and efficiency estimations, respectively. According to that finding, the developed ANN has a good property that it can be used in place of the designed generator, especially when the generator mathematical model is required. In addition, since power and efficiency are important for present applications, the present tool can be used to estimate the data for those characteristics of the machines and even it can be beneficial for the applications, where a nonlinear relationship among the power generation, generator efficiency, speed and load is required.     

Line start permanent magnet synchronous motors: Challenges and opportunities

Future energy challenges, likewise the environmental crises such as fossil fuel emissions and global warming urge the world to focus on energy saving programs more than ever. An effective way to face these challenges is to improve electric motors efficiency as one of the greatest energy consumption apparatuses in the world. Induction motors constitute, by far, the largest portion of electric motors both in terms of quantity and total power ratings among all electric motors. However, more efficient motor types gradually appear as alternatives. In this paper, line start permanent magnet motors as a powerful candidate with growing market are investigated in some details. The motor opportunities like high efficiency, high power factor and high power density are explored against the challenges associated with this motor including higher cost, extra manufacturing burden and transient and synchronization behaviors. Finally, some concluding comments and remarks are drawn for future research and manufacturing of line start permanent magnet motors.     

Compositive adaptive position control of induction motors based onpassivity theory

A passivity-based composite adaptive position control scheme for an induction motor is proposed in this paper. First, the dynamics of the induction motor are proved to be state strictly passive, and a composite adaptation algorithm is proposed to control the position of the induction motor. Then, the global stability of the induction motor position control system is formally proved by the passivity theory. Experimental results are provided to show that the good position tracking can be obtained without any information of the rotor flux. The proposed approach is robust to the variations of motor mechanical parameters and external load disturbances     

Performance analysis of permanent magnet synchronous motors. I.Dynamic performance

The dynamic performance of permanent magnet (PM) synchronous motors using damping and synchronizing torques is analyzed. A numerical algorithm is applied to obtain these torque components using a time-domain analysis of nonlinear systems. The effects of all electrical parameters are examined and the optimum values which give maximum internal damping are defined. It is demonstrated that the choice of the optimum values of these parameters results in well-damped oscillations with improved dynamical performance following load changes     

An enhanced adaptive neural network control scheme for powersystems

An enhanced adaptive neural network control scheme, based on the adaptive linear element (Adaline), is proposed and tested by applying it to a multimachine power system. Simulation results have shown that it is effective for different types of disturbances and over a wide range of operating conditions     

PEMFC的Elman神经网络建模与模糊神经网络控制

从质子交换膜燃料电池（PEMFC）实际应用的角度出发，采用Elman动态神经网络对PEMFC系统进行建模，以实验中采样到的PEMFC系统的工作温度输入输出数据训练网络，并采用动态反向传播学习算法根据误差不断调整网络参数直至达到要求精度。设计了一种适应模糊神经网络控制器，根据经验确定了初始隶属度函数和模糊规则，并采用自适应学习算法不断调整隶属度函数与模糊规则参数，使控制系统获得理想的输出。仿真实验以Elman神经网络模型为参考模型，使用自适应神经网络控制算法取得了较好的控制效果。总之，所设计的控制系统适合于控制PEMFC这样一类复杂非线性系统。     

High power fuel cell simulator based on artificial neural network

Artificial Neural Network (ANN) has become a powerful modeling tool for predicting the performance of complex systems with no well-known variable relationships due to the inherent properties. A commercial Polymeric Electrolyte Membrane fuel cell (PEMFC) stack (5 kW) was modeled successfully using this tool, increasing the number of test into the 7 inputs – 2 outputs-dimensional spaces in the shortest time, acquiring only a small amount of experimental data. Some parameters could not be measured easily on the real system in experimental tests; however, by receiving the data from PEMFC, the ANN could be trained to learn the internal relationships that govern this system, and predict its behavior without any physical equations. Confident accuracy was achieved in this work making possible to import this tool to complex systems and applications.     

基于人工神经网络的机车当量模型的研究

研究了当前我国机车检修的现状,在传统的机务检修基础上建立了机车当量公里的模型,将机车运行过程中的公里量、负荷量和时间量通过人工神经网络训练进行学习,形成了一种确定机车检修周期的新方法,解决了确定我国铁路机车检修周期问题。     

垂直轴永磁同步风力发电机侧整流器的终端滑模控制

滑模控制在提高滑模面速度时会有较大抖振。文章设计了一种新型滑模控制器,该滑模控制器不需要微分估计器及额外的滤波装置,就可抑制系统的抖振。结合矢量控制技术,采用文章方法分别设计永磁同步发电系统的速度外环和电流内环的滑模控制器,保证转速和电流在有限时间内跟踪相应的给定参考,实现最大风能捕获。仿真结果表明:与PI控制器和线性滑模控制器相比较,该控制策略实现风力发电机侧的最大风能捕获的同时,提高了系统的动静态性能。     

Reconstruction model for heat release rate based on artificial neural network

Optimizing the distribution of heat release rate (HRR) is the key to improve the performance of various combustors. However, limited by current diagnostic techniques, the spatial measurement of HRR in many realistic combustion devices is often difficult or even impossible. HRR prediction is theoretically possible through establishing correlations between HRR and other quantities (e.g., chemiluminescence intensity) that can be experimentally determined; however, up to now, few universal correlations have been established. A novel artificial neural network (ANN) approach was adopted to build the mapping relationship between the combustion heat release rate and the measurable chemiluminescent species. Proper orthogonal decomposition (POD) technology is used to extract the combustion physics and reduce the data of the spatial-temporally high-resolution combustion field. The correlation between the reduced-order HRR and chemiluminescent species is built using an ANN model. A unique segmentation approach was proposed to improve the training efficiency and accuracy. Validation in a supersonic hydrogen-oxygen nonpremixed flame proves the accuracy and efficiency of the proposed HRR reconstruction model based on the reduced-order POD method and data-driven ANN model.     

Inverse neural network based control strategy for absorption chillers

This paper proposes a novel, model-based control strategy for absorption cooling systems. First, a small-scale absorption chiller was modelled using artificial neural networks (ANNs). This model takes into account inlet and outlet temperatures as well as the flow rates of the external water circuits. The configuration 9-6-2 (9 inputs, 6 hidden and 2 output neurons) showed excellent agreement between the prediction and the experimental data (R² > 0.99 and RMSE < 0.05%). This type of ANN model is used to explain the behaviour of the system when operating conditions are measured and these measurements are available. A control strategy was also developed by using the inverse artificial neural network (ANNi) method. For a particular output (cooling load) the ANNi calculates the optimal unknown parameter(s) (controlling temperatures and flow rates). An optimization method was used to fit the unknown parameters of the ANNi method. The very low percentage of error and short computing time make this methodology suitable for the on-line control of absorption cooling systems.     

Proton exchange membrane fuel cell-powered bidirectional DC motor control based on adaptive sliding-mode technique with neural network estimation

Proton exchange membrane fuel cell (PEMFC), according to its merits of high energy density, zero emission, and low noise, has been widely applied in industrial appliances. A full bridge converter is used to implement PEMFC-powered DC motor bidirectional rotation in this paper. For the sake of the regulations of DC motor angular velocity as well as bus voltage, an adaptive backstepping sliding-mode control (ABSMC) technique integrated with Chebyshev neural network (CNN) is proposed. Based on the equivalent-circuit method, the control-oriented model of the PEMFC-powered motor system is structured. By constructing Lyapunov function, the adaptive laws and control laws can be obtained to achieve bus voltage and angular velocity regulations simultaneously. Moreover, the proposed neural network is applied to estimate the uncertainties of the system through orthogonal basis Chebyshev polynomials. To highlight the advantages of proposed technique, a proportional-integral (PI) control was introduced subsequently and two controllers were compared via numerical simulations. The simulation results demonstrate that CNN estimation method in conjunction with backstepping sliding-mode shows fast and accurate response even though the existence of system uncertainties and external disturbances.     

基于永磁同步电机逆系统解耦模糊滑模控制

基于逆系统理论和模糊滑模控制方法对永磁同步电动机调速系统进行了解耦控制研究,提出了一种新型的模糊推理滑模控制策略。根据永磁同步电动机调速系统的动态数学模型,证明了其逆系统的存在性,给出了永磁同步电动机逆系统解耦的模糊滑模控制器设计方法,系统仿真结果表明,该控制策略在不加剧滑动模态抖动的前提下,有效增强了系统的抗负载扰动能力,改善了永磁同步电动机系统的控制性能。     

High performance drive of DC brushless motors using neural network

In this paper, a multi-layer neural network (NN) architecture is proposed for the identification and control of DC brushless motors operating in a high performance drives environment. The NN in the proposed structure performs two functions. The first is to identify the nonlinear system dynamics at all times. Hence, detailed and elaborate models for the DC brushless machines are not needed. Furthermore, unknown nonlinear dynamics that are difficult to model such as load disturbances, system noise and parameter variations can be recognized by the trained neural network. The second function of the NN is to control the motor voltage so that the speed and position are made to follow pre-selected tracks (trajectories) at all times. The control action emulated by the NN is based on the indirect model reference adaptive control. A hardware laboratory setup is utilized to test and evaluate the proposed neural network structure. The paper shows, based on the laboratory test results, that the proposed neural network structure performance was good: the tracking accuracy was very high and the system robustness was quite evident even in the presence of random and severe disturbances     

直驱式永磁同步风力发电机的改进Backstepping控制策略

文章推导出网侧变换器在系统参数以及外界干扰影响下的动态模型,提出了改进的Backstepping控制策略,通过引入集总不确定量以及Sgn函数,提高了网侧变换器应对外界干扰的控制效果。此外,依据Lyapunov稳定性判定证明了所提控制策略的稳定性。仿真研究结果表明,文章所提出的控制策略有效改善了网侧变换器对直流母线电压的控制效果以及输入电网的电能质量。     

Nonlinear control of a permanent magnet synchronous motor withdisturbance torque estimation

This paper introduces a sensorless nonlinear control scheme for controlling the speed of a permanent magnet synchronous motor (PMSM) driving an unknown load torque. The states of the motor and disturbance torque are estimated via an extended nonlinear observer avoiding the use of mechanical sensors. The control strategy is an exact feedback linearization law, with trajectory tracking evaluated on estimated values of the PMSM states and the disturbance torque. The system performance is evaluated by simulations