个人信息Personal Information
教授
博士生导师
硕士生导师
性别:男
毕业院校:大连理工大学
学位:博士
所在单位:水利工程系
学科:水文学及水资源. 水利水电工程. 电力系统及其自动化. 计算机应用技术
联系方式:ctcheng@dlut.edu.cn
电子邮箱:ctcheng@dlut.edu.cn
A modular parallelization framework for power flow transfer analysis of large-scale power systems
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论文类型:期刊论文
发表时间:2018-07-01
发表刊物:JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY
收录刊物:SCIE、EI
卷号:6
期号:4
页面范围:679-690
ISSN号:2196-5625
关键字:Power flow transfer; Modular; Parallelization; Fork/Join framework; PSD-BPA
摘要: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.