No abstract

The paper deals with the issues to monitor the energy performance of Photo Voltaic (PV) fields by means of low cost hardware. In fact, the monitoring systems for low or medium rated power PV plants are often constituted by a limited number of sensors and low processing capacity. These systems allow a supervision of the PV fields when strong reductions of the produced energy happen, but they are ineffective to alert the end user about a gradual energy reduction. These issues are related to the natural ageing of PV modules, the dust or dirt accumulation on the PV modules, and so on. This paper proposes a methodology based on inferential tools, which return information about the correct operation of the PV field. The methodology needs an initial training that allows to define one or more reference strings, which will be used as benchmarks for future comparisons

Recently, Prognostics and Heath Management techniques have been deeply investigated with the aim to reduce life-cycle cost of products and systems. The increasing availability of condition monitoring data in substantial quantities for multitudes of homogeneous products and the need for generic algorithms that are applicable to complex systems motivates the development of new data-driven prognostic approaches. In this paper, two data-driven algorithms, one based on a statistical approach and another based on Neural Network, are discussed and tested for an application case. The analysis of the results has shown that both the considered approaches are characterized by reliable prediction performances on Remaining Useful Life calculation, thus resulting as potential tools for the application of a Condition-Based Maintenance strategy

A serious failure of a power transmission line can generate substantial financial losses due to the power outage. Consequently, utilities have a clear incentive to assess the actual condition of strategically important transmission lines, with the aim to minimize the risk of failures, enhance the maintenance practices and plan the development of the transmission network. This work focuses on the analysis of forced outages data for high-voltage overhead transmission lines (OHTL) in the Italian network during the period from 2008 to 2014. After a brief survey of the Italian network, the analysis has been carried out as follows: after a preliminary identification of the outage causes for OHTL, a new factor, namely the Severity Factor, has been introduced with the aim to drive the prioritization of the failure causes and the enhancement of the maintenance activities. Finally, an evaluation of the reliability, availability, safety and maintainability (RAMS) of the Italian OHTL network has been performe

No abstract