Fully GPU‐based electromagnetic transient simulation considering large‐scale control systems for system‐level studies. Issue 11 (24th July 2017)

• Record Type: Journal Article
• Authors: Song, Yankan; Chen, Ying; Huang, Shaowei; Xu, Yin; Yu, Zhitong; Marti, Jose R.
• Is Part Of: IET generation, transmission & distribution. Volume 11:Issue 11(2017)
• Page Start: 2840
• Page End: 2851
• Publication Date: 2017-07-24
• Subjects: graphics processing units -- distributed power generation -- directed graphs -- power distribution control -- EMTP; GPU‐based electromagnetic transient simulation -- large‐scale control systems -- large‐scale power systems -- system‐level EMT simulations -- fine‐grained parallel algorithm -- compute unified device architecture -- graphics processing units -- unified GPU kernels -- layered direct acyclic graphs -- LDAG -- fused multiply‐add operations -- FMA operations -- single‐instruction‐multiple‐threads pattern -- distributed generators; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2016.2078
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : As more generators and loads are integrated by power electronic converters with complicated controls, electromagnetic transients (EMTs) simulation becomes an important tool for studying dynamic characteristics of large‐scale power systems. To accelerate system‐level EMT simulations, a fine‐grained parallel algorithm on graphics processing units (GPU) is proposed. By decomposing the computational models of the EMT simulation into heterogeneous, homogeneous and network solution computations, the simulations are mapped into three unified GPU kernels. To incorporate control signals and non‐linear features of electrical components, heterogeneous computations are formulated as layered direct acyclic graphs (LDAG) of primitive operations. An LDAG kernel is designed to carry out theses primitive operations efficiently by grouped threads. Then, homogeneous computations for state updates of electrical components are modelled as sets of fused multiply‐add (FMA) operations, which are concurrently processed by an FMA kernel. Moreover, a hybrid network solution kernel is designed to solve the network equations, which can adaptively select dense or sparse solvers. Large‐scale test systems are created and simulated on an NVIDIA K20x GPU. The results show that the proposed GPU‐based EMT simulations are accurate and achieve 10x speedups over the CPU‐based ones.