CAREER: Intelligent Information Processing for Power Systems

本文研究了电力公司如何在不引入大量额外测量的情况下提取有关瞬时负荷组成的信息。要采用的统计方法允许从现有测量的总和中分离出单个负载。这些测量通常是不同类型负载和发电机的叠加，例如，在变电站一级获得。这种方法被称为“盲源分离”或“独立分量分析”（ICA），它只需要最小的假设，如负荷的独立性和电流的线性叠加，这是由于实际电网中负荷分布的基本特性而可以假设的。ICA已经成功地应用于解决技术问题，虽然它从未被用于电力系统，但初步数据表明，该方法提供了一种强大而可靠的方法来实现负载分布的分离和识别。并推测该方法适用于谐波注入识别。在电力系统分析、运行和控制的众多应用中，ICA可能成为实现数据降维的有力方法。

This proposal the question of how an electric power utility can extract information about the composition of the instantaneous load without having to introduce a multitude of additional measurements is studied. A statistical method to be employed allows to separate individual loads out of the aggregate of existing measurements. These measurements are usually superposition of different types of loads and generators, as obtained, for instance, at a substation level. The method called "Blind Source Separation" or "Independent Component Analysis" (ICA) requires only minimal assumptions, like independence of loads and linear superposition of currents, which can be assumed due to the basic properties of load distribution in realistic power networks. ICA has been applied successfully for the solution of technical problems previously, and, although it has never been employed for power systems, preliminary data given indicate that the method provides a powerful and reliable way of achieving load profile separation and identification. It is also conjectured that this method is suitable for harmonic injection identification. More generally, it is suggested that ICA may become a powerful method to achieve dimensionality reduction of data in a multitude of applications in power system analysis, operations and control.