System Strength Assessment Based on Multi-task Learning

IF 6.9 2区 工程技术 Q2 ENERGY & FUELS CSEE Journal of Power and Energy Systems Pub Date : 2023-12-28 DOI:10.17775/CSEEJPES.2023.00440
Baoluo Li;Shiyun Xu;Huadong Sun;Zonghan Li;Lin Yu
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Abstract

Increase in permeability of renewable energy sources (RESs) leads to the prominent problem of voltage stability in power system, so it is urgent to have a system strength evaluation method with both accuracy and practicability to control its access scale within a reasonable range. Therefore, a hybrid intelligence enhancement method is proposed by combining the advantages of mechanism method and data driven method. First, calculation of critical short circuit ratio (CSCR) is set as the direction of intelligent enhancement by taking the multiple renewable energy station short circuit ratio as the quantitative indicator. Then, the construction process of CSCR dataset is proposed, and a batch simulation program of samples is developed accordingly, which provides a data basis for subsequent research. Finally, a multi-task learning model based on progressive layered extraction is used to simultaneously predict CSCR of each RESs connection point, which significantly reduces evaluation error caused by weak links. Predictive performance and anti-noise performance of the proposed method are verified on the CEPRI-FS-102 bus system, which provides strong technical support for real-time monitoring of system strength.
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基于多任务学习的系统强度评估
可再生能源(RES)渗透率的增加导致电力系统电压稳定性问题突出,因此迫切需要一种既准确又实用的系统强度评估方法,将其接入规模控制在合理范围内。因此,结合机制法和数据驱动法的优点,提出了一种混合智能增强法。首先,以多可再生能源电站短路率为量化指标,将临界短路率(CSCR)的计算作为智能提升的方向。然后,提出了 CSCR 数据集的构建过程,并据此开发了样本批量仿真程序,为后续研究提供了数据基础。最后,利用基于渐进分层提取的多任务学习模型,同时预测各 RESs 连接点的 CSCR,大大降低了因薄弱环节造成的评估误差。所提方法的预测性能和抗噪声性能在 CEPRI-FS-102 总线系统上得到了验证,为实时监测系统强度提供了有力的技术支持。
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来源期刊
CiteScore
11.80
自引率
12.70%
发文量
389
审稿时长
26 weeks
期刊介绍: The CSEE Journal of Power and Energy Systems (JPES) is an international bimonthly journal published by the Chinese Society for Electrical Engineering (CSEE) in collaboration with CEPRI (China Electric Power Research Institute) and IEEE (The Institute of Electrical and Electronics Engineers) Inc. Indexed by SCI, Scopus, INSPEC, CSAD (Chinese Science Abstracts Database), DOAJ, and ProQuest, it serves as a platform for reporting cutting-edge theories, methods, technologies, and applications shaping the development of power systems in energy transition. The journal offers authors an international platform to enhance the reach and impact of their contributions.
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