DynaLiRD: A dataset for dynamic line rating of overhead transmission lines, utilizing meteorological data and grid parameters based on the IEEE 738-2012 standard.

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief Pub Date : 2025-09-15 eCollection Date: 2025-10-01 DOI:10.1016/j.dib.2025.112065

Najmul Alam, M A Rahman, Md Rashidul Islam

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引用次数: 0

Abstract

This article presents DynaLiRD, a comprehensive dataset for dynamic line rating (DLR) of the Trang-Thap Cham 220 kV overhead transmission line. The DLR values are computed using the IEEE 738-2012 standard based on historical meteorological data such as ambient temperature, wind speed and direction, and global horizontal irradiance as well as detailed line parameters including conductor type, diameter, length, and elevation. To enhance the dataset's applicability in cybersecurity and machine learning research, adversarially perturbed data is included using the fast gradient sign method (FGSM) and basic iterative method (BIM) under varying perturbation intensities. This dataset is essential for DLR estimation, dynamic thermal rating (DTR) forecasting, renewable energy integration into the grid, machine learning (ML) applications, infrastructure planning, energy policy development, and cybersecurity vulnerability investigation. Its structured format and inclusion of both clean and adversarial data make it valuable for evaluating the resilience of data-driven energy systems.

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dynamalird：基于IEEE 738-2012标准，利用气象数据和电网参数的架空输电线路动态线路额定值数据集。

本文介绍了一套全面的220千伏架空输电线路动态线路额定值（DLR）数据集。DLR值是根据IEEE 738-2012标准计算的，该标准基于历史气象数据，如环境温度、风速和风向、全球水平辐照度以及详细的线路参数，包括导线类型、直径、长度和高程。为了增强数据集在网络安全和机器学习研究中的适用性，在不同的扰动强度下，使用快速梯度符号法（FGSM）和基本迭代法（BIM）包括对抗扰动数据。该数据集对于DLR估计、动态热评级（DTR）预测、可再生能源整合到电网、机器学习（ML）应用、基础设施规划、能源政策制定和网络安全漏洞调查至关重要。它的结构化格式和包含清洁和对抗数据使其对评估数据驱动的能源系统的弹性有价值。

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

Data in Brief MULTIDISCIPLINARY SCIENCES-

CiteScore

3.10

自引率

0.00%

发文量

996

审稿时长

70 days

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