Being able to quantify energy production losses in photovoltaic (PV) systems is important in order to reduce the risk associated with investing in PV. Two such loss components are degradation rates and soiling losses. However, in systems where both these phenomena exist, quantifying them is not straightforward because of their combined effect on the power output. In this article, we propose an algorithm that iteratively decomposes a performance index time series of a PV system into a soiling component, a degradation component, and a seasonal component. This makes it possible to simultaneously estimate soiling losses and degradation rates of PV systems. Bootstrapping is used to estimate confidence intervals so that both data uncertainty and model uncertainty is taken into account. Based on simulated data we show that this method makes more accurate estimates of soiling losses and degradation rates than relevant state-of-the-art methods.
As the deployment rate of PV power plants continues to soar, the need for robust, scalable methods for performance analytics increases. In this paper, we demonstrate the usefulness of one approach for quantifying soiling rates in utility-scale PV power plants endogenously, i.e., directly from the production data. The temperature corrected performance ratio, normalized to a clean state, is used to derive the soiling ratio (SR). Cleaning events, caused by either rain or manual cleaning, are automatically detected by positive shifts in the running median of the SR time series. Soiling rates are then estimated by the rate of change of the SR between the cleaning events, which is determined by linear regression. The method is validated on data from three utility-scale PV power plants in the Middle East, yielding soiling rates that are in the range 0%-0.18%/day at least 50% of the time, with a median of 0.1%/day.
Recent trends in global networks are leading toward service-oriented architectures and sensor networks. On one hand of the spectrum, this means deployment of services from numerous providers to form new service composites, and on the other hand this means emergence of Internet of things. Both these kinds belong to a plethora of realms and can be deployed in many ways, which will pose serious problems in cases of abuse. Consequently, both trends increase the need for new approaches to digital forensics that would furnish admissible evidence for litigation. Because technology alone is clearly not sufficient, it has to be adequately supported by appropriate investigative procedures, which have yet become a subject of an international consensus. This paper therefore provides appropriate a holistic framework to foster an internationally agreed upon approach in digital forensics along with necessary improvements. It is based on a top-down approach, starting with legal, continuing with organizational, and ending with technical issues. More precisely, the paper presents a new architectural technological solution that addresses the core forensic principles at its roots. It deploys so-called leveled message authentication codes and digital signatures to provide data integrity in a way that significantly eases forensic investigations into attacked systems in their operational state. Further, using a top-down approach a conceptual framework for forensics readiness is given, which provides levels of abstraction and procedural guides embellished with a process model that allow investigators perform routine investigations, without becoming overwhelmed by low-level details. As low-level details should not be left out, the framework is further evaluated to include these details to allow organizations to configure their systems for proactive collection and preservation of potential digital evidence in a structured manner. The main reason behind this approach is to stimulate efforts on an internationally agreed "template legislation," similarly to model law in the area of electronic commerce, which would enable harmonized national implementations in the area of digital forensics.
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