在逆变器占主导地位的配电网中实现逆变器模型数字孪生的创新调谐算法研究

X. Song, T. Jiang, S. Schlegel, D. Westermann
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引用次数: 5

摘要

本文将采用基于神经网络的创新方法来构建逆变器的数字孪生。利用数字孪生(DT)模型可以实现逆变器的替换,可以作为替换物来模拟逆变器的运行情况。本文分为两部分:首先,对机电暂态逆变器的建模进行了阐述。然后,对所提出的方法进行了阐述,即首先对逆变器控制器进行在线PI调谐,然后使用基于神经网络的辨识器来逼近逆变器的非线性泛函动态状态。PI调谐器的设计是基于模型输出与测量数据的参考输出之间的偏差。然后,根据差值,调谐器可以计算出适当的电流和电压控制器参数来跟踪参考模型的动态行为。而神经网络辨识器则是通过神经网络复制参考模型的动态特征,该神经网络首先在线下训练大量的测试数据,然后应用于在线调优。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Investigation of inventive Tuning Algorithm for the realization of Digital Twins of Inverter Model in Inverter-dominated Power Distribution Grid
In this paper, the inventive methods based on the neural network will be applied to construct the digital twins of the inverter. By utilizing the digital twins (DT) model, the inverter can be replaced, which is able to be as the substitution to simulate the operation of the inverter. The paper can be arranged as two parts: First of all, the modelling of the inverter in electromechanical transient will be illustrated. Afterwards, the proposed methods are clarified, which are, firstly, an online PI tuner for the controllers of the inverter and then an neural network based identifier is operated to approximate the nonlinear functional dynamic state of it. The design of PI tuner is based on the deviation between the output of the model and the reference output from measurement data. Then, according to the difference, the tuner can calculate the appropriate parameter of the current and voltage controller to track the dynamic behaviour of the reference model. The NN identifier is, however, to replicate the dynamic character of the reference model by NN which is initially trained offline with extensive test data and afterwards is applied to online tuning.
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