"ANFIS Input\u2013Output Dataset for DFIG-Based Wind Energy Systems"

"This dataset provides real-time input\u2013output data for the development and validation of Adaptive Neuro-Fuzzy Inference System (ANFIS) controllers applied to a Doubly Fed Induction Generator (DFIG)\u2013based wind energy conversion system. The data were generated using an OPAL-RT OP5700 real-time simulator implementing an IEEE 39-bus power system interfaced with a grid-connected DFIG wind model. A Model-in-the-Loop (MiL) validation framework was employed to capture realistic system dynamics under variable wind speed conditions. The dataset includes controller error signals, change in error, ANFIS outputs, DC-link voltage, and active\/reactive power responses. It is structured using an 80%\u201320% training\u2013validation split and supports reproducible research in intelligent control, neuro-fuzzy systems, and wind energy integration"

面向基于双馈感应发电机（Doubly Fed Induction Generator, DFIG）的风能转换系统，本数据集可为自适应神经模糊推理系统（Adaptive Neuro-Fuzzy Inference System, ANFIS）控制器的开发与验证提供实时输入输出数据。该数据由OPAL-RT OP5700实时仿真器生成，该仿真器搭建了与并网型DFIG风电模型对接的IEEE 39节点电力系统。研究采用模型在环（Model-in-the-Loop, MiL）验证框架，以捕捉变风速工况下的真实系统动态特性。数据集涵盖控制器误差信号、误差变化量、ANFIS输出、直流母线电压以及有功/无功功率响应数据。该数据集采用80%训练集、20%验证集的划分方式构建，可为智能控制、神经模糊系统及风电并网领域的可复现研究提供支撑。