TY - JOUR
T1 - Tracking the performance of photovoltaic systems
T2 - a tool for minimising the risk of malfunctions and deterioration
AU - Spiliotis, Evangelos
AU - Legaki, Nikoletta Zampeta
AU - Assimakopoulos, Vassilios
AU - Doukas, Haris
AU - El Moursi, Mohamed Shawky
N1 - Funding Information:
Part of the work presented is based on research conducted within the project ‘OPTIMising the energy USe in cities with smart DSS (OPTIMUS)’, which has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement no. 608703. The work has also been supported by the ‘EU-GCC Clean Energy Technology Network’, European Commission – FPI service contract number PI/2015/370817 (http:// www.eugcc-cleanergy.net). The content of the paper is the sole responsibility of its authors and does not necessarily reflect the views of the European Commission.
Publisher Copyright:
© The Institution of Engineering and Technology 2018
PY - 2018/5/21
Y1 - 2018/5/21
N2 - The environmental and economic impact of photovoltaic (PV) systems is continuously growing, serving as an effective alternative energy source. Yet, failures and underperformance, e.g. due to soiling and deterioration, can significantly affect PV production and shrink the capacity available. This becomes an important issue, especially when the plant is not easily accessible for manual checking. Typical monitoring tools can help energy managers to deal with such issues. However, their diagnostics might be misleading as reduced performance could also be caused by low radiation and other relative factors, which are difficult to identify given the non-linear and stochastic relation of energy production and weather variables. In addition, accurate component-based models that use local weather measurements as inputs are not always applicable. In this regard, a methodological approach for tracking the performance of PV systems is proposed, which uses an artificial neural network, trained using reported system data and irradiation predictions. Possible abnormalities are identified through the model and alerts are generated to proceed with maintenance actions. The approach is implemented into a decision support system for smart cities, demonstrating encouraging results.
AB - The environmental and economic impact of photovoltaic (PV) systems is continuously growing, serving as an effective alternative energy source. Yet, failures and underperformance, e.g. due to soiling and deterioration, can significantly affect PV production and shrink the capacity available. This becomes an important issue, especially when the plant is not easily accessible for manual checking. Typical monitoring tools can help energy managers to deal with such issues. However, their diagnostics might be misleading as reduced performance could also be caused by low radiation and other relative factors, which are difficult to identify given the non-linear and stochastic relation of energy production and weather variables. In addition, accurate component-based models that use local weather measurements as inputs are not always applicable. In this regard, a methodological approach for tracking the performance of PV systems is proposed, which uses an artificial neural network, trained using reported system data and irradiation predictions. Possible abnormalities are identified through the model and alerts are generated to proceed with maintenance actions. The approach is implemented into a decision support system for smart cities, demonstrating encouraging results.
UR - http://www.scopus.com/inward/record.url?scp=85046272897&partnerID=8YFLogxK
U2 - 10.1049/iet-rpg.2017.0596
DO - 10.1049/iet-rpg.2017.0596
M3 - Article
AN - SCOPUS:85046272897
SN - 1752-1416
VL - 12
SP - 815
EP - 822
JO - IET Renewable Power Generation
JF - IET Renewable Power Generation
IS - 7
ER -
