Development of a Laboratory-Scale Digital Twin for Continuous Monitoring and Performance Assessment of Photovoltaic Panels and Wind Turbines

Digital twins integrate physical assets, sensing infrastructure, and computational models for continuous monitoring and operational assessment. This study presents a laboratory-scale Digital Twin for a physically constructed hybrid renewable-energy prototype comprising a ۱۰ W photovoltaic (PV) panel and a ۲ W wind turbine. All datasets reported here were obtained from synchronized experimental measurements of the prototype's electrical, thermal, optical, and rotational variables. Python was used only for data acquisition, synchronization, preprocessing, Digital Twin computation, visualization, and forecasting; it did not generate the experimental data. The framework preserves separate provenance for measured sensor data and model-derived estimates and provides timestamp validation, unit conversion, data-quality assessment, residual generation, topology verification, and weather-informed forecasting. Software verification included replay of laboratory records, bounded component-model checks, structural and end-to-end checks, and an automated suite in which all ۵۷ tests passed. Demonstrated laboratory functionality is separated from field-level claims: traceable irradiance calibration, an experimentally measured turbine power curve, quantified sensor uncertainty, and long-term seasonal validation remain ongoing tasks. The principal contribution is an interpretable architecture linking experimental measurements, physical models, residual evidence, data quality, model readiness, and forecast uncertainty. The physical prototype and its measured datasets provide a reproducible basis for future condition monitoring, fault diagnosis, and renewable-energy performance assessment.

کلیدواژه ها:

digital twin ، photovoltaic panel ، wind turbine ، experimental measurements ، continuous monitoring ، condition assessment