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

M. Davari
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引用次数: 2

Abstract

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.
用于评估PHIL测试精度的工业功率放大器动力学:通过线性系统识别方法的实验方法
在电源硬件在环(PHIL)数字仿真测试中,在数字实时仿真平台上实现的电源系统接口点(POI)的参考信号控制下,功率器件(也称为被测器件(DUT))与功率放大器进行虚拟电源交换。事实上,功率放大器(也称为网格模拟器)是任何PHIL测试的组成部分,其动态特性极大地影响着PHIL测试的准确性。工业功率放大器的动力学肯定不是一个理想的传递函数,即统一。事实上,这将降低测试的准确性,特别是当电力系统研究的感兴趣频率范围在功率放大器的动态频率响应范围内时。因此,有一个工业功率放大器的动态是非常有用的,以判断准确性的PHIL测试。本文以某工业功率放大器的实验结果为基础,采用不同的线性系统辨识方法提取了工业功率放大器的数学线性离散模型。设计输入信号、预处理数据、估计时延、估计模型阶数和参数、计算置信区间、表示模型频域以及验证不同的模型。本文采用了ARX、ARMAX、BJ和OE估计模型,这些模型均受益于预测误差法(PEM)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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