The objective of this work is to design and test a real-time solar hot water (SHW) fault detection system that can reliably detect anticipated and unforeseen faults using only those sensors that are normally required to operate a system. The fault detection system includes a data acquisition module and a hierarchical Adaptive Resonance Theory (ART)-based neural network fault detection module. The data acquisition system logs the collector fin temperature and the water tank heat exchanger outlet temperature. The detection module uses a hierarchical ART neural network that can detect faults and classify them by level of severity. The hierarchical ART neural network is trained with information collected from a verified solar hot water system TRNSYS (Transient Systems Simulation program) model. The TRNSYS model uses weather data for Albuquerque, NM, extracted from the National Solar Radiation Data Base (NSRDB), for the 5-year period 2000–2004. The neural network is trained under different weather conditions. The simulation and experimental results show that the trained fault detection system has the capability to detect expected faults including pump faults, impeller degradation, thermosyphon and potential unexpected ones. Simulated and experimental test results are presented.
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ASME 2011 5th International Conference on Energy Sustainability
August 7–10, 2011
Washington, DC, USA
Conference Sponsors:
- Advanced Energy Systems Division and Solar Energy Division
ISBN:
978-0-7918-5468-6
PROCEEDINGS PAPER
Real-Time Fault Detection for Solar Hot Water Systems Using Adaptive Resonance Theory Neural Networks Available to Purchase
Hongbo He,
Hongbo He
University of New Mexico, Albuquerque, NM
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David Menicucci,
David Menicucci
University of New Mexico, Albuquerque, NM
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Thomas Caudell,
Thomas Caudell
University of New Mexico, Albuquerque, NM
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Andrea Mammoli
Andrea Mammoli
University of New Mexico, Albuquerque, NM
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Hongbo He
University of New Mexico, Albuquerque, NM
David Menicucci
University of New Mexico, Albuquerque, NM
Thomas Caudell
University of New Mexico, Albuquerque, NM
Andrea Mammoli
University of New Mexico, Albuquerque, NM
Paper No:
ES2011-54885, pp. 1059-1065; 7 pages
Published Online:
March 13, 2012
Citation
He, H, Menicucci, D, Caudell, T, & Mammoli, A. "Real-Time Fault Detection for Solar Hot Water Systems Using Adaptive Resonance Theory Neural Networks." Proceedings of the ASME 2011 5th International Conference on Energy Sustainability. ASME 2011 5th International Conference on Energy Sustainability, Parts A, B, and C. Washington, DC, USA. August 7–10, 2011. pp. 1059-1065. ASME. https://doi.org/10.1115/ES2011-54885
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