A Virtual Environment for Remote Testing of Complex Systems

Complex systems, realized by integration of several components or subsystems, pose specific problems to simulation environments. It is, in fact, desirable to simulate the complex system altogether, and not component by component, since the operation of the single part depends on the surrounding system and an early verification can prevent damages and save time for modifications. The availability of detailed and validated models of the single parts is therefore critical. This task may be difficult to achieve. In fact, in industrial applications, where a system can be a mix of different devices produced by different manufacturers, the physical device may not be accessible to the modeler for proprietary or safety concerns. Starting from this point, the idea of creating a virtual environment able to test the real single component remotely, employing simulators with remote signal processing capability, has been considered. In this paper a methodology for remote model validation is presented. The effectiveness of the approach is experimentally verified locally and remotely. For the remote testing, in particular, the physical device under test is located at the Politecnico di Milano, Italy, and the Virtual Test Bed model is located at the University of South Carolina

A root cause analysis and a risk evaluation of PV balance of system failures

The Photovoltaic (PV) system is divided mainly into two subsystems; PV modules and a Balance of System (BoS) subsystems. This work shows two approaches for a reliability analysis on the subsystem level of aBoS: Failure mode effects criticality analysis (FMECA) and a Markov Process. FMECA concerns the root causes of failures and introduces prioritization numbers to highlight critical components of a BoS. Meanwhile, a Markov process is a reliability methodology that aims to predict the probability of success and failure of a BoS. In this way, a Markov process is a supportive tool for helping decision-makers to judge the criticality of failures associated with the operation of PV systems. Results show that the PV inverter contributes significantly to the failures of a BoS. Accordingly, further investigations are conducted on a PV inverter to prioritize the maintenance activities by determining the risk priority number of its component failures through quantitative CA. The novelty of the proposed methodologies stems from analyzing the roots of failure causes of BoS components and estimating the probability of failure of these components in order to improve the early development of a BoS, enhance maintenance management, and satisfy the demanding reliability by electric utilities

A nonlinear predictor for the supervision of photovoltaic strings performances

In this paper, a nonlinear predictor of the electrical power produced by a PV string is proposed. The first phase of the approach is the training of the predictor, during which four characteristic parameters are determined. Such coefficients are representative of the string under study and define its electrical signature (identikit). Once trained the model, when new monitoring data are available, the mismatch between the forecasted and measured electrical power can be assumed as a reliable marker of the performances of the string, since the greater the mismatch, the worse the string efficiency. The analysis of the forecasting error, therefore, enables the detection of losses of energy production. In particular, a strength of the proposed approach is the possibility to distinguish the losses due to aging phenomena from the losses due to the dust or dirt accumulation. The method has been tested and validated for a real case study and the obtained results are presented in the paper

Misurare l'Affidabilità: Sollecitazioni e degrado

Il lavoro introduce l’importanza, attraverso le misure di affidabilità, di conoscere la relazione di legame tra la sollecitazione applicata esternamente ad un dispositivo, o ad un materiale, ed il comportamento del materiale stesso. La risposta del materiale alla sollecitazione esterna determina il processo di degrado del componente e quindi la condizione di guasto

A data-driven prognostic approach based on statistical similarity: An application to industrial circuit breakers

In this paper, a data-driven prognostic algorithm for the estimation of the Remaining Useful Life (RUL) of a product is proposed. It is based on the acquisition and exploitation of run-to-failure data of homogeneous products, in the followings referred as fleet of products. The algorithm is able to detect the set of products (sub-fleet of products) showing highest degradation pattern similarity with the one under study and exploits the related monitoring data for a reliable prediction of the RUL. In particular, a novel methodology for the sub-fleet identification is presented and compared with other solution found in literature. The results obtained for a real application case as Medium and High Voltage Circuit Breaker, have shown a high prognostic power for the algorithm, which therefore represents a potential tool for an effective Predictive Maintenance (PdM) strategy