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Fuzzy logic and neural network based advanced control and estimation techniques in power electronics and AC drives.

机译:电力电子和交流变频器中基于模糊逻辑和神经网络的高级控制和估计技术。

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The dissertation presents, examines and analyzes advanced control and estimation techniques in power electronics and ac drives. It constitutes four projects where such artificial intelligence tools were extensively used. A variable speed wind generation system was developed, where three fuzzy logic controllers were used for efficiency optimization and for performance enhancement control. The controller FLC-1 searches the generator speed on-line so that the aerodynamic efficiency of the wind turbine can be optimized. A second fuzzy controller, FLC-2, programmed the machine flux by on-line search so as to optimize the machine-converter system efficiency. A third fuzzy controller, FLC-3, performed robust speed control against turbine oscillatory torque and wind vortex. Next, a neural network was applied for estimation of feedback signals in an induction motor drive, which has some distinct advantages when compared to DSP based implementation. A feedforward neural network received the machine terminal signals at the input and calculated flux, torque and unit vectors at the output, which were then used in the control of a direct vector-controlled drive system. The application of fuzzy logic to the estimation of power electronic waveforms was taken into consideration for distorted line current waves in a TRIAC dimmer and in a three-phase diode rectifier feeding an inverter-machine load. Fuzzy logic estimation was applied to assess the rms current, fundamental rms current, displacement factor and power factor. Both the rule base and relational approaches were used for estimation of the above parameters. The estimated values were then compared with the actual values, indicating good accuracy. Finally, the development of a speed and flux sensorless vector-controlled induction motor drive was considered. The stator flux oriented drive started at zero speed in indirect vector control mode, transited to direct vector control mode as the speed developed, and then transited back to indirect vector control at zero speed. The vector control used stator flux orientation in both indirect and direct vector control modes with the stator resistance variation compensated by measurement of stator temperature. The problem of integration at low stator frequency was solved by cascaded low-pass filters with programmable time constants.
机译:本文介绍,检查和分析了电力电子和交流变频器中的高级控制和估计技术。它由四个项目组成,其中广泛使用了此类人工智能工具。开发了变速风力发电系统,其中三个模糊逻辑控制器用于效率优化和性能增强控制。控制器FLC-1在线搜索发电机转速,从而可以优化风力涡轮机的空气动力学效率。第二个模糊控制器FLC-2通过在线搜索对机器磁通进行编程,以优化机器-变频器系统的效率。第三个模糊控制器FLC-3对涡轮振荡扭矩和风涡旋进行了鲁棒的速度控制。接下来,将神经网络应用于感应电动机驱动器中的反馈信号估计,与基于DSP的实现相比,它具有一些明显的优势。前馈神经网络在输入端接收机器终端信号,并在输出端接收计算出的磁通,转矩和单位矢量,然后将其用于直接矢量控制的驱动系统的控制中。对于TRIAC调光器和向逆变器负载供电的三相二极管整流器中的扭曲线电流波,考虑了将模糊逻辑应用于功率电子波形的估计。应用模糊逻辑估计来评估均方根电流,基本均方根电流,位移因数和功率因数。规则库和关系方法都用于估计以上参数。然后将估计值与实际值进行比较,表明准确性较高。最后,考虑了速度和磁通的无传感器矢量控制感应电动机驱动器的开发。定子磁链定向驱动器以间接矢量控制模式从零速开始,随着速度的发展而转变为直接矢量控制模式,然后又以零速转回到间接矢量控制。矢量控制在间接和直接矢量控制模式下都使用了定子磁通定向,并且通过测量定子温度来补偿定子电阻变化。通过级联的具有可编程时间常数的低通滤波器解决了低定子频率下的积分问题。

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