论文标题
部分可观测时空混沌系统的无模型预测
A Review of High-Performance Computing and Parallel Techniques Applied to Power Systems Optimization
论文作者
论文摘要
储层计算是预测湍流的有力工具,其简单的架构具有处理大型系统的计算效率。然而,其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间,然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用,该系统模拟了湍流的若干特征。我们表明,使用径向基函数作为非线性投影器,即使只有部分观测并且不知道控制方程,也能稳健地捕捉复杂的系统非线性。最后,我们表明,当测量稀疏、不完整且带有噪声,甚至控制方程变得不准确时,我们的网络仍然可以产生相当准确的预测,从而为实际湍流系统的无模型预测铺平了道路。
The accelerating technological landscape and drive towards net-zero emission made the power system grow in scale and complexity. Serial computational approaches for grid planning and operation struggle to execute necessary calculations within reasonable times. Resorting to high-performance and parallel computing approaches has become paramount. Moreover, the ambitious plans for the future grid and IoT integration make a shift towards utilizing Cloud computing inevitable. This article recounts the dawn of parallel computation and its appearance in power system studies, reviewing the most recent literature and research on exploiting the available computational resources and technologies today. The article starts with a brief introduction to the field. The relevant hardware and paradigms are then explained thoroughly in this article providing a base for the reader to understand the literature. Later, parallel power system studies are reviewed, reciting the study development from older papers up to the 21st century, emphasizing the most impactful work of the last decade. The studies included system stability studies, state estimation and power system operation, and market optimization. The reviews are split into \ac{CPU} based,\ac{GPU} based, and Cloud-based studies. Finally, the state-of-the-art is discussed, highlighting the issue of standardization and the future of computation in power system studies.