A modular parallelization framework for power flow transfer analysis of large-scale power systems

作者：Cheng, Chuntian; Luo, Bin^*; Shen, Jianjian; Liao, Shengli

来源：Journal of Modern Power Systems and Clean Energy, 2018, 6(4): 679-690.

DOI：10.1007/s40565-017-0354-4

• Power flow transfer
• Modular
• Parallelization
• Fork/Join framework
• PSD-BPA

Power flow transfer (PFT) analysis under various anticipated faults in advance is important for securing power system operations. In China, PSD-BPA software is the most widely used tool for power system analysis, but its input/output interface is easily adapted for PFT analysis, which is also difficult due to its computationally intensity. To solve this issue, and achieve a fast and accurate PFT analysis, a modular parallelization framework is developed in this paper. Two major contributions are included. One is several integrated PFT analysis modules, including parameter initialization, fault setting, network integrity detection, reasonableness identification and result analysis. The other is a parallelization technique for enhancing computation efficiency using a Fork/Join framework. The proposed framework has been tested and validated by the IEEE 39 bus reference power system. Furthermore, it has been applied to a practical power network with 11052 buses and 12487 branches in the Yunnan Power Grid of China, providing decision support for large-scale power system analysis.

• 出版日期2018-7
• 单位大连理工大学