Synthetic Power Quality Disturbance (PQD) Dataset of Single and Combined Disturbances Generated in Accordance with IEEE 1159 Specifications

Obtaining real PQD event signals directly from the electrical grid poses significant challenges for classification studies. To address this limitation, synthetic data generation becomes imperative. In this study, all generated PQD signals adhere strictly to the specifications outlined in IEEE 1159, ensuring the research's validity and applicability by closely emulating real-world PQD occurrences within the electrical grid. The proposed approach meticulously aligns all parameters of the synthesized PQD signals with the guidelines established in IEEE 1159-2019, guaranteeing that the synthetic dataset accurately reflects the characteristics and complexities of actual PQD events. This meticulous adherence to industry standards enhances the dataset's reliability and utility for classification tasks, providing researchers with a valuable resource for studying and analyzing power quality disturbances in electrical systems.直接从电网获取真实的电能质量扰动（Power Quality Disturbances, PQD）事件信号，给分类研究带来了显著挑战。为解决这一局限，合成数据生成变得至关重要。本研究中，所有生成的PQD信号均严格遵循IEEE 1159标准规定的技术规范，通过精准模拟电网中真实发生的PQD事件，保障了研究的有效性与适用性。本研究提出的方法将合成PQD信号的所有参数与IEEE 1159-2019标准确立的技术指南进行细致对齐，确保合成数据集能够准确反映真实PQD事件的特征与复杂性。这种对行业标准的严格遵循，提升了该数据集在分类任务中的可靠性与实用价值，为研究人员分析电力系统中的电能质量扰动提供了宝贵的研究资源。

搜集汇总

背景与挑战

背景概述

该数据集是一个合成电能质量扰动数据集，包含单一和复合形式的扰动信号，严格按照IEEE 1159规范生成。数据集提供原始数据和对应标签，适用于监督学习任务，支持电能质量领域的分类算法开发与评估，可用于信号处理、异常检测和故障诊断等研究。