Data-driven adaptive Lyapunov function based graphical deep convolutional neural network for smart grid congestion management

IF 3.3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research Pub Date : 2024-10-14 DOI:10.1016/j.epsr.2024.111163

J Christy , Pandia Rajan Jeyaraj

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引用次数: 0

Abstract

Optimal power flow by leveraging network grid topology will ensure stable operation of the smart grid. Energy management in grid-connected systems aimed to reduce computational non-linearities and ensure reliable operation of the smart grid. The conventional method manages congestion with optimal scheduling for every 10–15 min. Hence congestion in the smart grid occurs during secured energy distribution. In smart grid, instant congestion and energy management are needed. This research, work is devoted to a novel data-driven adaptive Lyapunov function with a Graphical Deep Convolutional Neural Network (GDCNN) regulated optimal flow by accurate energy management. By employing novel Graph theory-based network, the congestion data are obtained to train the proposed GDCNN. A Comparison of obtained results with existing baseline methods has been carried for claiming the novelties of proposed GDCNN. It is observed, that compared to existing machine learning-based extended subspace identification techniques. Our method has better optimal power regulation within 1.8 s by controlling power sources. Also, numerical simulation on IEEE 68 bus system shows the proposed GDCNN have superior performance, reliability, and optimal energy management. This by integrating the benefits of adaptive Lyapunov function and graphical convolutional network.

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基于数据驱动的自适应 Lyapunov 函数图形深度卷积神经网络用于智能电网拥塞管理

利用网络电网拓扑结构优化电力流将确保智能电网的稳定运行。并网系统中的能量管理旨在减少计算非线性，确保智能电网的可靠运行。传统方法通过每 10-15 分钟的优化调度来管理拥塞。因此，智能电网中的拥塞发生在安全的能量分配过程中。在智能电网中，需要即时的拥塞和能源管理。这项研究致力于利用图形深度卷积神经网络（GDCNN）的新型数据驱动自适应 Lyapunov 函数，通过精确的能源管理调节最佳流量。通过采用基于图论的新型网络，获得了拥堵数据来训练所提出的 GDCNN。为了证明所提出的 GDCNN 的新颖性，将所获得的结果与现有的基线方法进行了比较。结果表明，与现有的基于机器学习的扩展子空间识别技术相比，我们的方法能更好地实现最优电力调节。我们的方法通过控制电源，在 1.8 秒内实现了更好的最佳功率调节。此外，在 IEEE 68 总线系统上进行的数值仿真表明，所提出的 GDCNN 具有卓越的性能、可靠性和最佳能源管理。这得益于自适应 Lyapunov 函数和图形卷积网络的综合优势。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Electric Power Systems Research 工程技术-工程：电子与电气

CiteScore

7.50

自引率

17.90%

发文量

963

审稿时长

3.8 months

期刊介绍： Electric Power Systems Research is an international medium for the publication of original papers concerned with the generation, transmission, distribution and utilization of electrical energy. The journal aims at presenting important results of work in this field, whether in the form of applied research, development of new procedures or components, orginal application of existing knowledge or new designapproaches. The scope of Electric Power Systems Research is broad, encompassing all aspects of electric power systems. The following list of topics is not intended to be exhaustive, but rather to indicate topics that fall within the journal purview. • Generation techniques ranging from advances in conventional electromechanical methods, through nuclear power generation, to renewable energy generation. • Transmission, spanning the broad area from UHV (ac and dc) to network operation and protection, line routing and design. • Substation work: equipment design, protection and control systems. • Distribution techniques, equipment development, and smart grids. • The utilization area from energy efficiency to distributed load levelling techniques. • Systems studies including control techniques, planning, optimization methods, stability, security assessment and insulation coordination.