Abstract
This paper presents a novel data-driven approach, based on sensor network analysis in Photovoltaic (PV) power plants, to unveil hidden precursors in failure modes. The method is based on the analysis of signals from PV plant monitoring, and advocates the use of graph modeling techniques to reconstruct and investigate the connectivity among PV field sensors, as is customary for Complex Network Analysis (CNA) approaches. Five month operation data are used in the present study. The results showed that the proposed methodology is able to discover specific hidden dynamics, also referred to as emerging properties in a Complexity Science perspective, which are not visible in the observation of individual sensor signal but are closely linked to the relationships occurring at the system level. The application of exploratory data analysis techniques on those properties demonstrated, for the specific plant under scrutiny, potential for early fault detection.
Highlights
The cumulative global photovoltaic (PV) capacity has been growing exponentially around the world over recent years
The field sensors are modeled using graph theory as a complex network, where the nodes are represented by the signals from sensors and the edge are evaluated with non-linear statistical correlation functions applied to the time series pairs
Some typical complex network measurements are applied to extrapolate synthetic properties from the functional graphs
Summary
The cumulative global photovoltaic (PV) capacity has been growing exponentially around the world over recent years. In the decade 2005–2015, the solar PV generation capacity in the EU has increased from 1.9 GW to 95.4 GW [1]. As per the Italian market, in June 2013 the Italian public company GSE (Gestore dei Servizi Energetici) officially announced the discontinuation of the last Feed-in-Tariff incentive after its cap of 6700 million euro was reached [2]. The end of such subsidies has led to new attention being focused on PV plant performance management, lifetime and availability, with a view to reduce operating and maintenance costs [3].
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