High-performance computing for static security assessment of large power systems

IF 3.2 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Connection Science Pub Date : 2023-10-04 DOI:10.1080/09540091.2023.2264537
Venkateswara Rao Kagita, Sanjaya Kumar Panda, Ram Krishan, P. Deepak Reddy, Jabba Aswanth
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Abstract

Contingency analysis (CA) is one of the essential tools for the optimal design and security assessment of a reliable power system. However, its computational requirements rise with the growth of distributed generations in the interconnected power system. As CA is a complex and computationally intensive problem, it requires a fast and accurate calculation to ensure the secure operation. Therefore, efficient mathematical modelling and parallel programming are key to efficient static security analysis. This paper proposes a parallel algorithm for static CA that uses both central processing units (CPUs) and graphical processing units (GPUs). To enhance the accuracy, AC load flow is used, and parallel computation of load flow is done simultaneously, with efficient screening and ranking of the critical contingencies. We perform extensive experiments to evaluate the efficacy of the proposed algorithm. As a result, we establish that the proposed parallel algorithm with high-performance computing (HPC) computing is much faster than the traditional algorithms. Furthermore, the HPC experiments were conducted using the national supercomputing facility, which demonstrates the proposed algorithm in the context of N−1 and N−2 static CA with immense power systems, such as the Indian northern regional power grid (NRPG) 246-bus and the polish 2383-bus networks.
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大型电力系统静态安全评估的高性能计算
应急分析是进行可靠电力系统优化设计和安全评估的重要工具之一。然而,随着互联电力系统中分布式电源的增加,其计算量也随之增加。CA是一个复杂且计算量大的问题,为了保证安全运行,需要快速准确的计算。因此,高效的数学建模和并行编程是高效的静态安全分析的关键。本文提出了一种同时使用中央处理器(cpu)和图形处理器(gpu)的静态CA并行算法。为提高计算精度,采用交流潮流,同时进行潮流并行计算,有效筛选和排序临界事故。我们进行了大量的实验来评估所提出算法的有效性。结果表明,采用高性能计算(HPC)的并行算法比传统算法运行速度快得多。此外,利用国家超级计算设施进行了HPC实验,验证了所提出的算法在大型电力系统(如印度北部地区电网(NRPG) 246总线和波兰2383总线网络)的N−1和N−2静态CA环境下的性能。
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来源期刊
Connection Science
Connection Science 工程技术-计算机:理论方法
CiteScore
6.50
自引率
39.60%
发文量
94
审稿时长
3 months
期刊介绍: Connection Science is an interdisciplinary journal dedicated to exploring the convergence of the analytic and synthetic sciences, including neuroscience, computational modelling, artificial intelligence, machine learning, deep learning, Database, Big Data, quantum computing, Blockchain, Zero-Knowledge, Internet of Things, Cybersecurity, and parallel and distributed computing. A strong focus is on the articles arising from connectionist, probabilistic, dynamical, or evolutionary approaches in aspects of Computer Science, applied applications, and systems-level computational subjects that seek to understand models in science and engineering.
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