A Data Analytics Perspective of the Clarke and Related Transforms in Power Grid Analysis
A Data Analytics Perspective of the Clarke and Related Transforms in Power Grid Analysis
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2018-07
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通讯作者：
D. Mandic;S. Kanna;Yili Xia;Ahmad Moniri;A. Constantinides
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作者：
D. Mandic;S. Kanna;Yili Xia;Ahmad Moniri;A. Constantinides
随着智能电网在发电和配电中的作用越来越重要，负担得起、可靠的电力是现代社会和经济的基础。为了充分发挥其优势，现代智能电网的分析需要电力工程师与信号处理和机器学习专家的密切合作和融合。目前的分析技术通常是从电路理论的角度出发的；这种方法仅适用于在名义条件下运行的完全平衡的系统，对于数据科学家来说并不明显-这对于动态不平衡智能电网的分析是不可行的，因为数据分析不仅非常适合，而且也是必要的。一种弥合电路理论和数据分析之间差距的共同语言，以及各自的专家社区，将是向前迈出的自然一步。为此，我们从子空间、潜在分量和空间频率分析框架重温Clarke变换和相关变换，以建立标准三相变换和现代数据分析之间的基本关系。我们证明了Clarke变换的物理解释是一种“空间”降维技术，它等价于平衡系统的主成分分析(PCA)，但对于动态不平衡系统(如智能电网)是次优的，而相关的Park变换则执行进一步的“时间”降维。这种观点为在电网研究中使用信号处理和机器学习开辟了许多新的途径，并为创新的优化、转换和分析技术铺平了道路，这些技术是从标准电路理论原理中无法获得的，正如这项工作通过同时频率估计和故障检测的可能性所展示的那样。
Affordable and reliable electric power is fundamental to modern society and economy, with the Smart Grid becoming an increasingly important factor in power generation and distribution. In order to fully exploit it advantages, the analysis of modern Smart Grid requires close collaboration and convergence between power engineers and signal processing and machine learning experts. Current analysis techniques are typically derived from a Circuit Theory perspective; such an approach is adequate for only fully balanced systems operating at nominal conditions and non-obvious for data scientists - this is prohibitive for the analysis of dynamically unbalanced smart grids, where Data Analytics is not only well suited but also necessary. A common language that bridges the gap between Circuit Theory and Data Analytics, and the respective community of experts, would be a natural step forward. To this end, we revisit the Clarke and related transforms from a subspace, latent component, and spatial frequency analysis frameworks, to establish fundamental relationships between the standard three-phase transforms and modern Data Analytics. We show that the Clarke transform admits a physical interpretation as a "spatial dimensionality" reduction technique which is equivalent to Principal Component Analysis (PCA) for balanced systems, but is sub-optimal for dynamically unbalanced systems, such as the Smart Grid, while the related Park transform performs further "temporal" dimensionality reduction. Such a perspective opens numerous new avenues for the use Signal Processing and Machine Learning in power grid research, and paves the way for innovative optimisation, transformation, and analysis techniques that are not accessible to arrive at from the standard Circuit Theory principles, as demonstrated in this work through the possibility of simultaneous frequency estimation and fault detection.