开放电力系统数据集和开放仿真引擎:机器学习应用综述

IF 3.2 Q3 ENERGY & FUELS
Ignacio Aravena;Chih-Che Sun;Ranyu Shi;Subir Majumder;Weihang Yan;Jhi-Young Joo;Le Xie;Jiyu Wang
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引用次数: 0

摘要

机器学习(ML)模型在多个领域取得成功背后的一个主要因素是用于训练和基准测试的大型、标记和组织良好的数据集的可用性和可访问性。相比之下,电网数据集面临三大挑战:(i)现实世界的数据往往受到监管约束、隐私原因或安全问题的限制,难以获取和使用;(ii)为解决这些限制而创建的合成数据集往往信息不完整,使用专门工具发布,使更广泛的社区无法访问;并且(iii)由于电力系统社区之外不知道开源模拟器,因此很难通过模拟为非专家生成输入输出数据集。这项调查通过为在这一领域冒险的研究人员提供公开可用的数据集和模拟器的入口点来解决这些挑战。我们回顾了开源电网数据、机器模型、消费者需求概况、可再生能源发电数据和逆变器模型的现状。我们还研究了开源电力系统模拟器,这对于生成高质量,高保真度的电网数据集至关重要。我们的目标是为克服数据稀缺性提供基础,并向数据集和模拟器的结构化网络推进,以支持电力系统的ML开发。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Open Power System Datasets and Open Simulation Engines: A Survey Toward Machine Learning Applications
A major factor behind the success of machine learning (ML) models in multiple domains is the availability and accessibility of large, labeled, and well-organized datasets for training and benchmarking. In comparison, power grid datasets face three major challenges: (i) real-world data is often restricted by regulatory constraints, privacy reasons, or security concerns, making it difficult to obtain and work with; (ii) synthetic datasets, which are created to address these limitations, often have incomplete information and are released using specialized tools, making them inaccessible to the broader community; and, (iii) input-output datasets are difficult to generate through simulation for non-experts because open-source simulators are not known outside the power system community. This survey addresses these challenges by serving as an entry point to publicly available datasets and simulators for researchers venturing in this area. We review the current landscape of open-source power network data, machine models, consumer demand profiles, renewable generation data, and inverter models. We also examine open-source power system simulators, which are crucial for generating high-quality, high-fidelity power grid datasets. We aim to provide a foundation for overcoming data scarcity and advance towards a structured web of datasets and simulators to support the development of ML for power systems.
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来源期刊
CiteScore
7.80
自引率
5.30%
发文量
45
审稿时长
10 weeks
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