The rising need of energy to improve the quality of life has paved the way for the development and the incentive of different kinds of renewable energy technologies. In particular, the recent increase in the number of installed PhotoVoltaic (PV) plants has boosted the marketing of new monitoring systems designed to take under control the energy production of PV plants. In this paper, we present an intelligent monitoring system, called SUNInspector, which resorts to spatio-temporal data mining techniques, in order to monitor energy productions of PV plants and detect real-time possible plant faults. SUNInspector uses spatio-temporal patterns, called trend clusters, to model the trends according to the energy production of the PV plants varies depending on the region where it is installed (spatial dependence) and the period of the year of the measurements (temporal dipendence). Each time a PV plant transmits its energy production measurement, the risk of a plant fault is measured by evaluating the persistence of an high difference between the real production and the expected production. A case study with PV plants distributed over the South of Italy is illustrated.
An intelligent system for real time fault detection in PV plants
APPICE, ANNALISA;MALERBA, Donato;
2012-01-01
Abstract
The rising need of energy to improve the quality of life has paved the way for the development and the incentive of different kinds of renewable energy technologies. In particular, the recent increase in the number of installed PhotoVoltaic (PV) plants has boosted the marketing of new monitoring systems designed to take under control the energy production of PV plants. In this paper, we present an intelligent monitoring system, called SUNInspector, which resorts to spatio-temporal data mining techniques, in order to monitor energy productions of PV plants and detect real-time possible plant faults. SUNInspector uses spatio-temporal patterns, called trend clusters, to model the trends according to the energy production of the PV plants varies depending on the region where it is installed (spatial dependence) and the period of the year of the measurements (temporal dipendence). Each time a PV plant transmits its energy production measurement, the risk of a plant fault is measured by evaluating the persistence of an high difference between the real production and the expected production. A case study with PV plants distributed over the South of Italy is illustrated.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
