Abstract
This paper presents a generic framework to evaluate and compare the quality of the uncertainties provided by probabilistic forecasts of power system state when used to perform security assessment for branch overloads. Besides exploiting advanced univariate and multivariate metrics that are traditionally used in weather prediction, the evaluation is complemented by assessing the benefits from exploiting probabilistic forecasts over the current practices of using deterministic forecasts of the system operating conditions. Another important feature of this framework is the provision of parameters tuning when applying flow probabilistic forecasts to perform security assessment for branch overloads. The quality and scalability of this framework is demonstrated and validated on recent historical data of the French transmission system. Although being developed to address branch overload problems, with proper adaptations, this work can be extended to other power system security problems.

Abstract
Visual exploration of the larynx represents a relevant technique for the early diagnosis of laryngeal disorders. However, visualizing an endoscopy for finding abnormalities is a time-consuming process, and for this reason much research has been dedicated to the automatic analysis of endoscopic video data. In this work we address the particular task of discriminating among informative laryngoscopic frames and those that carry insufficient diagnostic information. In the latter case, the goal is also to determine the reason for this lack of information. To this end, we analyze the possibility of training three different state-of-the-art Convolutional Neural Networks, but initializing their weights from configurations that have been previously optimized for solving natural image classification problems. Our findings show that the simplest of these three architectures not only is the most accurate (outperforming previously proposed techniques), but also the fastest and most efficient, with the lowest inference time and minimal memory requirements, enabling real-time application and deployment in portable devices.

Abstract
Since DC Motors are common components in engineering projects that involve process control, it is necessary for any student in this area to understand their concepts, construction and applications. This paper focuses on a series of Laboratory Experiments that were carried out in an Entry Level Unit of the Integrated Master Degree in Electrical and Computers Engineering of the Faculty of Engineering of the University of Porto, named Project FEUP. In this class, mandatory for all students, they learn to how use these motors, from basic concepts to the estimation modeling. The paper presents the developed kits that students use, the simplified model and examples of the experiments performed in some classes.

Abstract
IEC 61850 is a standard for the design and operation of electrical Substation Automation Systems (SAS) that defines how data may be transferred among Intelligent Electronic Devices (IEDs). The defined data models can be mapped into application protocols, such as SV or GOOSE, which may run upon high speed Ethernet LANs bridged by IEEE 802.1Q compliant switches. The communication system must cope with the timing requirements associated to protective relaying strategies. Given the time constrained nature of SAS applications, a thorough analysis of its timing behavior is required. In this paper, we propose an analytical model for the timing assessment of SV and GOOSE message exchanges in an IEC 61850 process bus. The proposed model allows the communication timing assessment, by analyzing the response time of each message stream of the SAS. This feature is an advantage for the expansion of the SAS, as it allows the evaluation at design time of the maximum number of IEDs that can be supported by the underlying communication system. The results from the proposed analytical model were validated for a typical IEC 61850 communication scenario, both through simulation and through an experimental assessment with IEC 61850 compliant equipment.

Abstract

Abstract
This paper presents an integrated approach for assessing the impact that distributed energy resources (DERs), including intermittent photovoltaic (PV) generation, might have on the reliability performance of power networks. A test distribution system, based on a typical urban MV and LV networks in the UK, is modelled and used to investigate potential benefits of the local renewable generation, demand-manageable loads and coordinated energy storage. The conventional Monte Carlo method is modified to include time-variation of electricity demand profiles and failure rates of network components. Additionally, a theoretical interruption model is employed to assess more accurately the moment in time when interruptions to electricity customers are likely to occur. Accordingly, the impact of the spatio-temporal variation of DERs on reliability performance is quantified in terms of the effect of network outages. The potential benefits from smart grid functionalities are assessed through both system- and customer-oriented reliability indices, with special attention to energy not supplied to customers, as well as frequency and duration of supply interruptions. The paper also discusses deployment of an intelligent energy management system to control local energy generation-storage-demand resources that can resolve uncertainties in renewable-based generation and ensure highly reliable and continuous supply to all connected customers.