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Validation Method for Simulation Model of Internet of Things-Aided Power System

机译:辅助电力系统互联网仿真模型的验证方法

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摘要

Validation of power system simulation models is essentially a similarity analysis problem based on multivariate time series. With the development of the internet of things (IoT) technology in the power system, the interoperability and integration of devices in the practical project are improved, and the cross interaction in the simulation process becomes more complex correspondingly. It is critical to explore the inherent correlation from the high dimensional data to evaluate the credibility and to locate the error of the simulation model. Thus, a model validation method based on factor analysis and the Prony method is proposed in this paper. Firstly, the multivariate time series of the simulation model and the practical/acknowledged system are replaced by a low number of common factors with physical meanings by factor analysis. Secondly, the modified adaptive Prony method is applied to extract the features of each common factor to ensure the best fitting of the non-stationary signal. Then the complete similarity evaluation model of the simulation system is established based on energy proportion, information entropy, and variance of the contribution rate. Finally, the error location is identified in the evaluation process based on the physical meaning of extracted features. The feasibility and effectiveness of the proposed method are verified by an application in the simulation model of a power electronics system developed in PSASP.
机译:电力系统仿真模型的验证基本上是基于多变量时间序列的相似性分析问题。随着电力系统的事物互联网(IOT)技术的发展,改善了实际项目中设备的互操作性和集成,并且模拟过程中的交叉相互作用相应地变得更加复杂。探索高维数据的固有关联至关重要,以评估可信度并定位模拟模型的错误。因此,本文提出了一种基于因子分析和PRONY方法的模型验证方法。首先,模拟模型的多变量时间序列和实际/确认系统被因因素分析的具有物理含义的少量常见因素所取代。其次,应用修改的自适应掌方法来提取每个常见因素的特征,以确保非静止信号的最佳拟合。然后,基于能量比例,信息熵和贡献率的方差建立仿真系统的完整相似性评估模型。最后,基于提取特征的物理含义,在评估过程中识别出错误位置。所提出的方法的可行性和有效性通过PSASP开发的电力电子系统的仿真模型中的应用验证。

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