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Dynamics of an industrial power amplifier for evaluating PHIL testing accuracy: An experimental approach via linear system identification methods

机译:评估PHIL测试精度的工业功率放大器的动力学:通过线性系统识别方法的实验方法

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In power-hardware-in-the-loop (PHIL) digital simulation testing, a power device, also known as device-under-test (DUT), is virtually exchanging power with a power amplifier governed by the reference signals coming from the point of interface (POI) in the power system implemented on a digital real-time simulation platform. Indeed, the power amplifier (also known as grid simulator) is the integral of any PHIL testing, and its dynamics are greatly impacting the accuracy of the PHIL testing. The dynamics of an industrial power amplifier is certainly not an ideal transfer function, i.e., unity. In fact, it is going to degrade the accuracy of the testing especially when the interested frequency range of the power system studies is within the frequency response of the power amplifier's dynamics. Consequently, having an industrial power amplifier's dynamics is very helpful in order to judge the accuracy of the PHIL testing. In this paper, experimental results of an industrial power amplifier have been used, and mathematical linear discrete-time models of the industrial power amplifier have been extracted using different linear system identification methods. Designing input signals, pre-processing data, estimating time delay, estimating model order and parameters, calculating confidence intervals, representing frequency-domain of models, and validating different models are shown in this paper. ARX, ARMAX, BJ, and OE estimated models, which benefit from prediction error method (PEM), are employed in this paper.
机译:在功率硬件在环(PHIL)数字仿真测试中,功率设备(也称为被测设备(DUT))实际上是与功率放大器交换功率,功率放大器受来自该点的参考信号控制数字实时仿真平台上实现的电力系统接口(POI)的设计。实际上,功率放大器(也称为网格模拟器)是任何PHIL测试不可或缺的一部分,其动态特性极大地影响了PHIL测试的准确性。工业功率放大器的动态特性当然不是理想的传递函数,即单位。实际上,这将降低测试的准确性,尤其是当电力系统研究的感兴趣频率范围在功率放大器动态特性的频率响应之内时。因此,具有工业功率放大器的动态特性对于判断PHIL测试的准确性非常有帮助。本文使用了工业功率放大器的实验结果,并使用不同的线性系统识别方法提取了工业功率放大器的数学线性离散时间模型。本文显示了设计输入信号,预处理数据,估计时间延迟,估计模型顺序和参数,计算置信区间,表示模型的频域以及验证不同的模型。本文采用了受益于预测误差法(PEM)的ARX,ARMAX,BJ和OE估计模型。

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