A Digital Twin Approach for Fault Diagnosis in Distributed Photovoltaic Systems
By Palak Jain, J. Poon, J. Singh, Costas J. Spanos, S. Sanders, Sanjib Kumar Panda. IEEE transactions on power electronics, 2020
388 citations recorded by the source corpus.
Rooftop and building-integrated distributed photovoltaic (PV) systems are emerging as key technologies for smart building applications. This paper presents the design methodology, mathematical analysis, simulation study, and experimental validation of a digital twin approach for fault diagnosis. We develop a digital twin that estimates the measurable characteristic outputs of a PV energy conversion unit (PVECU) in real time. The PVECU constitutes a PV source and a source-level power converter. The fault diagnosis is performed by generating and evaluating an error residual vector, which is the difference between the estimated and measured outputs. A PV panel-level power converter prototype is built to demonstrate how the sensing, processing, and actuation capabilities of the converter can enable effective fault diagnosis in real time. The experimental results show detection and identification of ten different faults in the PVECU. The time to fault detection (FD) in the power converter and the electrical sensors is less than 290 $\mu$s and the identification time is less than 4 ms. The time to FD and identification in the PV panel are less than 80 ms and 1.2 s, respectively. The proposed approach demonstrates higher fault sensitivity than that of existing approaches. It can diagnose a 20% drift in the electrical sensor gains and a 20% shading of a solar cell in the PV panel.
Subfields: Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment
View Paper: https://www.semanticscholar.org/paper/b69ca6ce97d1a900bbe0a4a0b51a01945fb2f7e7
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Citations in the corpus
- A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies (2022, background): “Five out of the 12 topcited papers concentrate on fault diagnostics and prognostics of physical assets using digital twin (Xu et al., 2019; Li et al., 2017; Glaessgen and Stargel, 2012; Jain et al., 2019; Tuegel et al., 2011).”
- Holistic Network Virtualization and Pervasive Network Intelligence for 6G (2023, background): “[100] Smart building Photovoltaic energy conversion units Estimating the status of photovoltaic energy conversion unit Improving the accuracy of fault detection”
- The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities (2021, methodology): “…PHM is accomplished for power and energy related components with the use of DTs, such as wind-turbine, [108], electric vehicle motor [61], photovoltaic systems [112], battery systems [113], plasma radiation detection in metal absorber–metal resistor bolometer [114], as discussed in Section VII-B.”
- A Review on Digital Twin Technology in Smart Grid, Transportation System and Smart City: Challenges and Future (2023, background): “In [36], the development of the DT to guide fault detection is investigated.”
- Digital Twin: Enabling Technologies, Challenges and Open Research (2020, background): “Twin technology allows the team to develop a Digital Twin that can estimate accurately faults relating to PV energy units in real-time [76].”
- Industrial applications of digital twins (2021, background): “— DT estimator of photovoltaic energy conversion unit outputs [46].”