Simulation Dataset of Partial Shading and Fault of a Photovoltaic Module

Micro-autonomous or remotely controlled drones are getting popular for search and rescue missions during natural or human-made disasters. A photovoltaic-based power source is ideal for prolonging the flight time and multi-role ability of the drone. However, during various lighting conditions and adverse operating conditions, the PV panels' performance can deteriorate and adversely affect micro-autonomous performance. Hence, a dataset for 10 GaAs/Ge Single Junction PV cells by Spectrolab is presented. The dataset is generated using python and LTSpice at various temperatures, configurations, lighting conditions, and open/short circuit faults. For modeling PV cells, 2-diode based equivalent circuit model is used. The dataset is generated using LTSpice and Python.

在自然或人为灾害的搜救任务中，微型自主无人机或遥控无人机的应用正日益广泛。基于光伏（Photovoltaic, PV）的供电系统是延长无人机续航时长、拓展其多任务执行能力的理想方案。然而，在不同光照条件与恶劣运行工况下，光伏面板的性能会出现衰减，进而对微型自主无人机的运行性能产生负面影响。为此，本研究公开了斯派克特拉博（Spectrolab）生产的10片砷化镓/锗（GaAs/Ge）单结光伏电池的数据集。该数据集通过Python与LTSpice生成，涵盖不同温度、电路配置、光照条件以及开路/短路故障工况下的各类样本数据。本次光伏电池建模采用基于双二极管的等效电路模型。

搜集汇总

背景与挑战

背景概述

该数据集是一个针对光伏模块局部阴影和故障的模拟数据集，专门为10个GaAs/Ge单结光伏电池生成，使用Python和LTSpice基于双二极管等效电路模型进行模拟，覆盖了多种温度、配置、光照条件和故障场景。数据集旨在支持机器学习和能源转换研究，帮助分析光伏面板在恶劣条件下的性能变化，适用于无人机等微自主系统的电源优化。