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Identification of hydraulic turbine governor system parameters based on Bacterial Foraging Optimization Algorithm

机译:基于细菌觅食优化算法的液压涡轮机调速系统参数识别

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摘要

Hydraulic turbine generating unit plays an important role in power system. An accurate hydraulic turbine governor system model is essential to analyze its stability and dynamic performance. In order to identify the parameters of the hydraulic turbine governor system model, a new approach of Bacterial Foraging Optimization Algorithm (BFOA) is introduced in this study. To improve the precision of the identification process, a modified objective function is proposed based on the measurement of gate opening, mechanical torque and generator speed from a simulated model. The improved objective function (IOF) and the conventional objective function (COF) are used in the identification and two sets of parameters are derived and compared. The results show that BFOA is effective in identification of hydraulic turbine governor system and parameters derived from the modified objective function have a higher accuracy.
机译:液压涡轮发电机在电力系统中起着重要作用。精确的液压涡轮机调速系统模型对于分析其稳定性和动态性能至关重要。为了识别液压涡轮机调速器系统模型的参数,本研究介绍了一种新的细菌觅食优化算法(BFOA)方法。为了提高识别过程的精度,基于从模拟模型的栅极开口,机械扭矩和发电机速度的测量来提出改进的目标函数。改进的目标函数(IOF)和传统的目标函数(COF)用于识别和两组参数来得出并进行比较。结果表明,BFOA有效地识别液压涡轮机调速器系统和来自改进的物镜函数的参数具有更高的精度。

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