Enhancing microgrid performance with AI-based predictive control: Establishing an intelligent distributed control system

IF 2 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Afshin Hasani, Hossein Heydari, Mohammad Sadegh Golsorkhi
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引用次数: 0

Abstract

Microgrids play a pivotal role in modern power distribution systems, necessitating precise control methodologies to tackle challenges such as performance instability, especially during islanding operations. This paper introduces an advanced control strategy that employs artificial intelligence, specifically deep neural network (DNN) predictions, to enhance microgrid performance, particularly in an islanding mode where voltage and frequency (VaF) deviations are critical concerns. By utilizing real-time data and historical trends, the proposed controller accurately forecasts power demand and generation patterns, enabling proactive planning and optimization of efficiency, reliability, and sustainability in microgrid management. One significant aspect of this approach is to establish an intelligent distributed control system that minimizes reliance on communication devices while ensuring that VaF remains within acceptable limits. Moreover, it consolidates the roles of primary and secondary controllers within the microgrid and facilitates the prediction of load changes and load injection processes. This capability significantly reduces microgrid VaF deviations, enhancing system performance through precise power distribution and balanced coordination among distributed generators. Consequently, it ensures the stability and reliability of the system. In summary, the integration of DNN-based predictive control represents a significant advancement in microgrid management, providing a solution to address performance challenges and optimize operational efficiency, reliability, and sustainability.

Abstract Image

利用基于人工智能的预测控制提高微电网性能:建立智能分布式控制系统
微电网在现代配电系统中发挥着举足轻重的作用,需要精确的控制方法来应对性能不稳定等挑战,尤其是在孤岛运行期间。本文介绍了一种先进的控制策略,该策略采用人工智能,特别是深度神经网络(DNN)预测,以提高微电网性能,尤其是在电压和频率(VaF)偏差是关键问题的孤岛模式下。通过利用实时数据和历史趋势,所提出的控制器可准确预测电力需求和发电模式,从而在微电网管理中实现效率、可靠性和可持续性的主动规划和优化。这种方法的一个重要方面是建立一个智能分布式控制系统,最大限度地减少对通信设备的依赖,同时确保 VaF 保持在可接受的范围内。此外,它还整合了微电网中主控制器和辅助控制器的作用,并有助于预测负荷变化和负荷注入过程。这一功能大大降低了微电网 VaF 偏差,通过精确的功率分配和分布式发电机之间的平衡协调提高了系统性能。因此,它能确保系统的稳定性和可靠性。总之,基于 DNN 的预测控制的集成代表了微电网管理的重大进步,为应对性能挑战、优化运行效率、可靠性和可持续性提供了解决方案。
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来源期刊
Iet Generation Transmission & Distribution
Iet Generation Transmission & Distribution 工程技术-工程:电子与电气
CiteScore
6.10
自引率
12.00%
发文量
301
审稿时长
5.4 months
期刊介绍: IET Generation, Transmission & Distribution is intended as a forum for the publication and discussion of current practice and future developments in electric power generation, transmission and distribution. Practical papers in which examples of good present practice can be described and disseminated are particularly sought. Papers of high technical merit relying on mathematical arguments and computation will be considered, but authors are asked to relegate, as far as possible, the details of analysis to an appendix. The scope of IET Generation, Transmission & Distribution includes the following: Design of transmission and distribution systems Operation and control of power generation Power system management, planning and economics Power system operation, protection and control Power system measurement and modelling Computer applications and computational intelligence in power flexible AC or DC transmission systems Special Issues. Current Call for papers: Next Generation of Synchrophasor-based Power System Monitoring, Operation and Control - https://digital-library.theiet.org/files/IET_GTD_CFP_NGSPSMOC.pdf
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