卷积神经网络与极端梯度提升的混合模型,用于评估集成了可再生能源的复合电力系统的可靠性

IF 1.6 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Electrical Engineering Pub Date : 2024-09-13 DOI:10.1007/s00202-024-02683-3
Chiranjeevi Yarramsetty, Tukaram Moger, Debashisha Jena
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引用次数: 0

摘要

本文介绍了一种通过将机器学习(ML)技术与连续蒙特卡罗仿真(SMCS)相结合来提高复合电力系统可靠性评估计算效率的方法。可再生能源(RER)与电力系统的整合正在快速增加。评估复合电力系统的可靠性有助于发现其运行中的任何缺陷。随着电力系统的运行变得更加波动和随机,有必要更新用于分析可靠性的工具。本文采用 SMCS 作为传统方法,因为它通过按时间顺序计算 RER 来得出结果。然而,SMCS 的计算量很大。ML 模型适合解决需要计算能力的复杂问题。我们提出了卷积神经网络(CNN)、卷积和极端梯度提升(ConXGB)以及卷积和随机森林(ConRF)的混合模型等 ML 技术,用于确定削减负荷的预期值和最小削减负荷量。建议的技术应用于测试系统 IEEE RTS-79。结果表明,ConvXGB 方法在计算综合可靠性指数方面既快速又准确。例如,与 SMCS 的 0.0021 LOLP 和 0.1794 MW EDNS 相比,它实现了 0.0025 LOLP 和 0.1850 MW EDNS,计算时间从 12900 秒减少到 5414 秒。
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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A hybrid model of convolutional neural network and an extreme gradient boosting for reliability evaluation in composite power systems integrated with renewable energy resources

This paper introduces an approach that enhances the computational efficiency of reliability assessment for composite power systems by integrating machine learning (ML) techniques with sequential monte carlo simulation (SMCS). Integration of renewable energy resources (RERs) into power systems is increasing at a rapid pace. Evaluating the reliability of composite power systems is helpful in identifying any deficiencies in their operation. As power systems operation becomes more fluctuating and stochastic, it is necessary to update the tools used to analyse reliability. In this paper, SMCS is used as a conventional method, as it provides results by taking chronological nature of RERs. However, SMCS is highly computational. ML models fit for solving complex problems that require computational power. ML techniques, such as convolutional neural network (CNN) and hybrib models of Convolutional and Extreme Gradient Boosting (ConXGB), and Convolutional and Random Forest (ConRF) are proposed to determine the expectation of load curtailment and minimum amount of load curtailments. The proposed technique is applied on test system IEEE RTS-79. Results indicate the ConvXGB method is fast and accurate in computing composite reliability indices. For instance, it achieved a Loss of Load Probability (LOLP) of 0.0025 and an Expected Demand Not Supplied (EDNS) of 0.1850 MW, compared to SMCS’s LOLP of 0.0021 and EDNS of 0.1794 MW while reducing computational time from 12900 to 5414 s. These results confirm the proposed method’s speed and accuracy, making it a robust solution for modern power system reliability evaluation.

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来源期刊
Electrical Engineering
Electrical Engineering 工程技术-工程:电子与电气
CiteScore
3.60
自引率
16.70%
发文量
0
审稿时长
>12 weeks
期刊介绍: The journal “Electrical Engineering” following the long tradition of Archiv für Elektrotechnik publishes original papers of archival value in electrical engineering with a strong focus on electric power systems, smart grid approaches to power transmission and distribution, power system planning, operation and control, electricity markets, renewable power generation, microgrids, power electronics, electrical machines and drives, electric vehicles, railway electrification systems and electric transportation infrastructures, energy storage in electric power systems and vehicles, high voltage engineering, electromagnetic transients in power networks, lightning protection, electrical safety, electrical insulation systems, apparatus, devices, and components. Manuscripts describing theoretical, computer application and experimental research results are welcomed. Electrical Engineering - Archiv für Elektrotechnik is published in agreement with Verband der Elektrotechnik Elektronik Informationstechnik eV (VDE).
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