基于联络线特征的分布式优化划分 电力系统的

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摘要翻译：
分布式优化算法的收敛性能对分布式求解最优潮流具有重要意义。在本文中，我们的目的是提供一些见解，如何划分一个电力系统，以实现高收敛速度的分布式算法求解一个最优潮流问题。我们分析了电力网络的几个特点，寻找一组合适的分区，以提高收敛性能。我们将网格建模为一个图，并基于边间图聚类对其进行分解。这种技术提供了几个分区。为了找到一种有效的分区，我们将聚类技术得到的分区进行合并，并根据连接相邻分区的联络线的特征对其进行分析。主要目标是找到与收敛速度有关的最佳分区集。我们采用分析目标级联(ATC)方法对优化子问题进行分布式求解。我们在IEEE118节点系统上对所提出的算法进行了测试。结果表明，在适当的划分下，算法收敛速度更快，而不适当的划分会导致大量的迭代
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英文标题：
《Tie-Line Characteristics based Partitioning for Distributed Optimization
  of Power Systems》
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作者：
Ali Mohammadi, Mahdi Mehrtash, Amin Kargarian, and Masoud Barati
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  The convergence performance of distributed optimization algorithms is of significant importance to solve optimal power flow (OPF) in a distributed fashion. In this paper, we aim to provide some insights on how to partition a power system to achieve a high convergence rate of distributed algorithms for the solution of an OPF problem. We analyzed several features of the power network to find a set of suitable partitions with the aim of convergence performance improvement. We model the grid as a graph and decompose it based on the edge betweenness graph clustering. This technique provides several partitions. To find an effective partitioning, we merge the partitions obtained by clustering technique and analyze them based on characteristics of tie-lines connecting neighboring partitions. The main goal is to find the best set of partitions with respect to the convergence speed. We deploy analytical target cascading (ATC) method to distributedly solve optimization subproblems. We test the proposed algorithm on the IEEE 118-bus system. The results show that the algorithm converges faster with a proper partitioning, whereas improper partitioning leads to a large number of iterations
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PDF链接：
https://arxiv.org/pdf/1805.09779

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关键词：分布式 联络线 Optimization Partitioning Applications distributed 分布式 划分 optimization 进行