Novel Data-Driven Distributed Learning Framework for Solving AC Power Flow for Large Interconnected Systems
Novel Data-Driven Distributed Learning Framework for Solving AC Power Flow for Large Interconnected Systems
复制标题
用于解决大型互连系统交流潮流的新型数据驱动分布式学习框架
DOI：
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发表时间：
2021
影响因子：
3.8
通讯作者：
Todd Hay
中科院分区：
文献类型：
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作者：
B. Vyakaranam;Kaveri Mahapatra;Xinya Li;Heng Wang;P. Etingov;Z. Hou;Q. Nguyen;Tony B. Nguyen;N. Samaan;M. Elizondo;Todd Hay
电力系统的最新发展导致了大规模互联系统的复杂性，并对在各种运行条件下进行安全评估研究提出了挑战。传统的基于模型的方法是计算密集型的，并且可能不满足实时应用的要求。本文提出了一种新的数据驱动框架，用于加速使用深度卷积神经网络（DCNN）为大型系统获得多个AC潮流（ACPF）解决方案的过程。DCNN模型使用来自系统的各种代表性潮流案例进行设计和训练，其输出可用于执行稳态安全评估研究。使用TensorFlow实现了多个图形处理单元（GPU）的分布式训练，以减少计算时间。建议的框架实施，以确定关键的总线和认识ACPF的情况下，预计会导致稳态总线电压违规。在西部电力协调理事会（WECC）2028系统上对该框架的有效性和可行性进行了评估。结果表明，所提出的框架是高度准确的，并具有良好的解释性，在执行各种输电规划和运行评估研究的大规模电网。
Recent advancement in power systems induces complexity in large-scale interconnected systems and poses challenges in performing security assessment studies at various operating conditions. Traditional model-based methods are computationally intensive and may not meet the requirements for real-time applications. This paper presents a novel data-driven framework for accelerating the process of obtaining multiple AC power flow (ACPF) solutions for large systems using deep convolutional neural networks (DCNN). DCNN models are designed and trained using various representative power flow cases from a system, whose outputs can be used to perform steady-state security assessment studies. Distributed training with multiple Graphical processing units (GPU)s is implemented using TensorFlow to reduce computation time. The proposed framework is implemented to identify critical buses and recognize the ACPF cases expected to cause steady-state bus voltage violations. The efficacy and feasibility of the proposed framework are evaluated on the Western Electricity Coordinating Council (WECC) 2028 system. Results demonstrate that the proposed framework is highly accurate and possess good interpretability in performing various transmission planning and operation assessment studies for large scale power networks.
影响因子：
6.6
作者：
Du, Yan;Li, Fangxing;Zheng, Tongxin
通讯作者：
Zheng, Tongxin