Implementing a Digital Twin-based fault detection and diagnosis approach for optimal operation and maintenance of urban distributed solar photovoltaics

Abstract

Over the next decades, solar energy power generation is anticipated to gain popularity because of the current energy and climate problems and ultimately become a crucial part of urban infrastructure. Furthermore, in order to facilitate the rapid deployment of solar production in sustainable cities, solar technologies must be digitalized and smart. This will increase their reliability and ensure their optimal performance. This work fits into this perspective and focuses on the effectiveness of processing data using the Digital Twin (DT) framework as a state-of-art information technology for urban distributed solar PV systems. Accordingly, the present paper deals with the DT of five Building-added Photovoltaic (BAPV) systems installed at the solar village in Benguerir city, Morocco. The 3D models were generated in the Rhinoceros platform, the solar AC electrical production of each photovoltaic system is calculated by the ladybug-plugin-integrated statistical model, within the grasshopper environment. Additionally, data assessment and visualization are processed through Python. Then, a hybrid model-based and data-driven fault detection and diagnosis (FDD) approach is proposed to identify and isolate anomalies for decentralized solar PV systems at the urban scale using monitoring and inspection techniques, namely Remote Sensors (RS) and real-time solar production monitoring system. The application of the DT concept for complex dynamic systems has shown its effectiveness in ensuring optimal operating conditions for the energy systems by measuring the spatiotemporal energy performance of distributed solar PV systems at an urban scale, evolving with the changing environment, and identifying degradation based on anomalies occurrence; Only then when the physical models need to be amended. Therefore, the main goal behind this work is to advance knowledge regarding research on condition monitoring of solar systems in cities while considering issues related to anomalies like the degradation phenomenon in PV modules over time. The findings of this study have proved that the state of health and monitoring of PV systems could be successfully detected and efficiently monitored using the combination of real time monitoring and remote aerial sensing.