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.

Power supply in stand-alone power systems, such those in non-grid connected islands, represents an important area of study in investigating smart energy concepts. In particular, the discontinuity in renewable energy availability and the mismatch with power demand are likely to hinder grid stability and overall system efficiency. Typically, the load-levelling relies on diesel engine gen-sets which suffer the modulation of power output resulting in increased operation costs and life time reduction. Although energy storage can complement power fluxes balance, a proper dispatch strategy is needed in order to improve diesel engine operations in Renewable Energy Source (RES) integrated systems. The present study investigates the merit of a dispatch strategy aiming at improving gen-set performance in a hybrid RES/storage/Diesel Engine Generator set (DEGS) power configuration. The proposed dispatch strategy is modelled in a transient simulation software, with hourly based analysis over a year period and is applied to a small island case study.