Machine learning-based inertia estimation in power systems: a review of methods and challenges

Q2 Energy
Santosh Diggikar, Arunkumar Patil, Siddhant Satyapal Katkar, Kunal Samad
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引用次数: 0

Abstract

The transformation of power systems is accelerating due to the widespread integration of renewable energy sources (RES) and the growing role of inverter-based generations (IBGs). This shift has significantly reduced rotational inertia, increasing the system’s vulnerability to frequency fluctuations during disturbances. Consequently, the accurate and adaptive estimation of inertia has become crucial for maintaining frequency stability and grid reliability. Traditional estimation methods, though effective in certain scenarios, struggle to capture the non-linear and dynamic behaviors of modern power systems, necessitating the adoption of advanced solutions. This review comprehensively explores machine learning (ML)-based methodologies for inertia estimation, emphasizing their adaptability, scalability, and real-time capabilities compared to conventional approaches. The study categorizes ML techniques into supervised learning (SL), unsupervised learning (USL), semi-supervised learning (SSL), and reinforcement learning (RL), highlighting their applications, advantages, and limitations. Advanced methodologies, such as hybrid and ensemble models, are examined for their effectiveness in overcoming challenges posed by noisy data, dynamic behaviors, and complex grid configurations. Some advanced techniques demonstrate proficiency in analyzing complex datasets and providing real-time insights into the evolving dynamics of inertia. In addition to evaluating existing approaches, the review identifies key research gaps and emerging trends, offering strategic guidance and important considerations for the development of innovative ML-driven inertia estimation methods. By addressing these challenges, this study aims to support the creation of adaptive and reliable tools that ensure effective grid management in an energy ecosystem increasingly dominated by RES.

Graphical abstract

电力系统中基于机器学习的惯性估计:方法与挑战综述
由于可再生能源(RES)的广泛整合和基于逆变器的世代(ibg)的日益重要的作用,电力系统的转型正在加速。这种转变大大降低了旋转惯性,增加了系统在干扰期间对频率波动的脆弱性。因此,准确和自适应的惯性估计对于保持电网的频率稳定性和可靠性至关重要。传统的估计方法虽然在某些情况下有效,但难以捕捉现代电力系统的非线性和动态行为,因此需要采用先进的解决方案。这篇综述全面探讨了基于机器学习(ML)的惯性估计方法,与传统方法相比,强调了它们的适应性、可扩展性和实时性。该研究将机器学习技术分为监督学习(SL)、无监督学习(USL)、半监督学习(SSL)和强化学习(RL),并强调了它们的应用、优势和局限性。先进的方法,如混合和集成模型,在克服噪声数据,动态行为和复杂的网格配置带来的挑战的有效性进行了检查。一些先进的技术展示了分析复杂数据集的熟练程度,并提供了对惯性演化动态的实时洞察。除了评估现有方法外,该综述还确定了关键的研究差距和新兴趋势,为开发创新的ml驱动惯性估计方法提供了战略指导和重要考虑因素。通过解决这些挑战,本研究旨在支持自适应可靠工具的创建,以确保在日益由res主导的能源生态系统中有效的电网管理
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
Energy Informatics
Energy Informatics Computer Science-Computer Networks and Communications
CiteScore
5.50
自引率
0.00%
发文量
34
审稿时长
5 weeks
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