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The System Integrity Protection Scheme (SIPS) is a specialized protection scheme which differs to a great extent from the common protection, especially in its main intention. It is designed for integrity protection, failure prevention and mitigation of disturbance consequences in the whole power system (PS) or modern distribution system (MDS) with a significant power of distributed generation (DG). The input parameters for establishing SIPS in PS are synchronized phasor measurements obtained from the Phasor Measurement Units (PMUs), as a globally acknowledged Smart Grid technology. The realization requires that SIPS is added to the existing Wide Area Monitoring, Protection and Control (WAMPAC), based on synchronized measurements. This paper proposes an algorithm that anticipates optimal PMU placement and then uses angular stability protection based on traditional IEDs supported by synchronized measurements. By using such approach, the system avoids possible blackouts due to the combined operation of the traditional protection and WAMPAC. The proposed SIPS system is tested on specific area in the Croatian transmission system integrating hydro power and wind energy converting power plants. Due to the increased power of renewable energy sources (RES), the proposed algorithm is applicable to modern distribution systems. [ABSTRACT FROM AUTHOR]

The paper presents results of a research made by teachers of undergraduate electrical engineering students in determining the significance of using didactic tools in order to increase the students` motivation to participate actively in the teaching process. Converters used in this research are designed and modelled by students themselves for laboratory power-electronics exercises. It is shown that using didactic tools improves understanding of complex subjects when the students have the opportunity to link practical exercises with the theoretical explanations given in lectures. [ABSTRACT FROM AUTHOR]

This paper proposes an approach to schedule and allocate regulating reserve in systems with a large share of generation from wind power plants (WPPs). The method for scheduling regulating power is based on the correction of day-ahead schedules planned by the known variations of the load- and wind-power from the previous hour. The correction term incorporates the tendency of changes for previous hours. In this way, more of the regulating power can be reserved during sudden changes in the intensity of the wind. Hence, available existing operational data are required, instead of historical data. The method for allocation of regulating power to regulating units is based on minimization of the transmission losses with optional correction according to economic criteria. The qualitative analysis of the relevant economic features is introduced, as well as the possibilities for applying advanced information and communications technology. The methods discussed were tested for the standard test network RTS 96 and for the Croatian transmission network. Test results showed considerable reduction of the unnecessary positive and negative regulating reserve with increased safety compared to the classic method. Furthermore, optimal allocation of regulating reserve reduced transmission losses for all the cases discussed. Reductions were more significant when the layouts of WPPs and regulating capacities were more geographically dispersed. [ABSTRACT FROM AUTHOR]

Interest in sports predictions as well as the public availability of large amounts of structured and unstructured data are increasing every day. As sporting events are not completely independent events, but characterized by the influence of the human factor, the adequate selection of the analysis process is very important. In this paper, seven different classification machine learning algorithms are used and validated with two validation methods: Train&Test and cross-validation. Validation methods were analyzed and critically reviewed. The obtained results are analyzed and compared. Analyzing the results of the used machine learning algorithms, the best average prediction results were obtained by using the nearest neighbors algorithm and the worst prediction results were obtained by using decision trees. The cross-validation method obtained better results than the Train&Test validation method. The prediction results of the Train&Test validation method by using disjoint datasets and up-to-date data were also compared. Better results were obtained by using up-to-date data. In addition, directions for future research are also explained. [ABSTRACT FROM AUTHOR]

Disruptions in the global market are influencing value and supply chains reminding businesses and industries that variability and diversity of supply chains may be essential for surviving and sustainability. Operations management of any business has to address these challenges in order to avoid any serious interruptions in supply of materials in production industries by seeking substitute inputs. At the same time, the technological development offers new materials with similar quality properties, making thereby the substitute material search more difficult in terms of selecting appropriate materials with a level of quality which is similar enough. Another aspect in shifting can be found in more social-related reasons addressing changes in the value chains like traceability, low carbonization, and a more customer-oriented approach, because of moving towards green digital business. In this sense the intention of this work was to propose an algorithm for customizing the process of identifying appropriate materials in production by relying on existing algorithms i.e., the Ashby mapping, big data, applying algorithms of data analysis based on exclusion criteria embracing transformation paradigms, for enabling customization of the material selection process. The proposed algorithm was applied on two case examples, demonstrating that diversity of materials plays an important role in addressing customization requests from customers. Consequently, understanding and implementing a customer-centric approach in various phases of the product life cycle contributes to a better response by businesses faced with issuing customized offerings. [ABSTRACT FROM AUTHOR]