877 research outputs found
a markov chain based model for wind power prediction in congested electrical grids
The large penetration of wind generators in existing electrical grids induces critical issues that are pushing the system operators to improve several critical operation functions, such as the security analysis and the spinning reserve assessment, with the purpose of mitigating the effects induced by the injected power profiles, which are ruled by the intermittent and not-programmable wind dynamics. Although numerous forecasting tools have been proposed in the literature to predict the generated power profiles in function of the estimated wind speed, further and more complex phenomena need to be investigated in order to take into account the effects of the forecasting uncertainty on power system operation. In order to deal with this issue, this paper proposes a probabilistic model based on Markov chains, which predicts the wind power profiles injected into the grid, considering the real generator model and the effects of the power curtailments imposed by the grid operator. Experimental results obtained on a real case study are presented and discussed in order to prove the effectiveness of the proposed method
Data-driven Models to Anticipate Critical Voltage Events in Power Systems
This paper explores the effectiveness of data-driven models to predict
voltage excursion events in power systems using simple categorical labels. By
treating the prediction as a categorical classification task, the workflow is
characterized by a low computational and data burden. A proof-of-concept case
study on a real portion of the Italian 150 kV sub-transmission network, which
hosts a significant amount of wind power generation, demonstrates the general
validity of the proposal and offers insight into the strengths and weaknesses
of several widely utilized prediction models for this application.Comment: In proceedings of the 11th Bulk Power Systems Dynamics and Control
Symposium (IREP 2022), July 25-30, 2022, Banff, Canad
A review of the enabling methodologies for knowledge discovery from smart grids data
The large-scale deployment of pervasive sensors and decentralized computing in modern smart
grids is expected to exponentially increase the volume of data exchanged by power system applications.
In this context, the research for scalable and flexible methodologies aimed at supporting rapid decisions
in a data rich, but information limited environment represents a relevant issue to address. To this aim,
this paper investigates the role of Knowledge Discovery from massive Datasets in smart grid computing,
exploring its various application fields by considering the power system stakeholder available data and
knowledge extraction needs. In particular, the aim of this paper is dual. In the first part, the authors
summarize the most recent activities developed in this field by the Task Force on “Enabling Paradigms
for High-Performance Computing in Wide Area Monitoring Protective and Control Systems” of the
IEEE PSOPE Technologies and Innovation Subcommittee. Differently, in the second part, the authors
propose the development of a data-driven forecasting methodology, which is modeled by considering
the fundamental principles of Knowledge Discovery Process data workflow. Furthermore, the described
methodology is applied to solve the load forecasting problem for a complex user case, in order to
emphasize the potential role of knowledge discovery in supporting post processing analysis in data-rich
environments, as feedback for the improvement of the forecasting performances
Phaseolus vulgaris extract ameliorates high-fat diet-induced colonic barrier dysfunction and inflammation in mice by regulating peroxisome proliferator-activated receptor expression and butyrate levels
Obesity is a health concern worldwide, and its onset is multifactorial. In addition to metabolic syndrome, a high-fat diet induces many deleterious downstream effects, such as chronic systemic inflammation, a loss of gut barrier integrity, and gut microbial dysbiosis, with a reduction of many butyrate-producing bacteria. These conditions can be ameliorated by increasing legumes in the daily diet. White and kidney beans (Phaseolus vulgaris L.) and their non-nutritive bioactive component phaseolamin were demonstrated to mitigate several pathological features related to a metabolic syndrome-like condition. The aim of the present study was to investigate the molecular pathways involved in the protective effects on the intestinal and liver environment of a chronic oral treatment with P. vulgaris extract (PHAS) on a murine model of the high-fat diet. Results show that PHAS treatment has an anti-inflammatory effect on the liver, colon, and cecum. This protective effect was mediated by peroxisome proliferator-activated receptor (PPAR)-α and γ. Moreover, we also observed that repeated PHAS treatment was able to restore tight junctions' expression and protective factors of colon and cecum integrity disrupted in HFD mice. This improvement was correlated with a significant increase of butyrate levels in serum and fecal samples compared to the HFD group. These data underline that prolonged treatment with PHAS significantly reduces some pathological features related to the metabolic syndrome-like condition, such as inflammation and intestinal barrier disruption; therefore, PHAS could be a valid tool to be associated with the therapeutic strategy
Experimental tests of hidden variable theories from dBB to Stochastic Electrodynamics
In this paper we present some of our experimental results on testing hidden
variable theories, which range from Bell inequalities measurements to a
conclusive test of stochastic electrodynamics
A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures
In recent years, the field of Deep Learning has seen many disruptive and
impactful advancements. Given the increasing complexity of deep neural
networks, the need for efficient hardware accelerators has become more and more
pressing to design heterogeneous HPC platforms. The design of Deep Learning
accelerators requires a multidisciplinary approach, combining expertise from
several areas, spanning from computer architecture to approximate computing,
computational models, and machine learning algorithms. Several methodologies
and tools have been proposed to design accelerators for Deep Learning,
including hardware-software co-design approaches, high-level synthesis methods,
specific customized compilers, and methodologies for design space exploration,
modeling, and simulation. These methodologies aim to maximize the exploitable
parallelism and minimize data movement to achieve high performance and energy
efficiency. This survey provides a holistic review of the most influential
design methodologies and EDA tools proposed in recent years to implement Deep
Learning accelerators, offering the reader a wide perspective in this rapidly
evolving field. In particular, this work complements the previous survey
proposed by the same authors in [203], which focuses on Deep Learning hardware
accelerators for heterogeneous HPC platforms
NEMO-SN1 Abyssal Cabled Observatory in the Western Ionian Sea
The NEutrinoMediterranean Observatory—Submarine
Network 1 (NEMO-SN1) seafloor observatory is located in
the central Mediterranean Sea, Western Ionian Sea, off Eastern Sicily (Southern Italy) at 2100-m water depth, 25 km from the harbor of the city of Catania. It is a prototype of a cabled deep-sea multiparameter observatory and the first one operating with real-time data transmission in Europe since 2005. NEMO-SN1 is also the first-established node of the European Multidisciplinary Seafloor Observatory (EMSO), one of the incoming European large-scale research infrastructures included in the Roadmap of the European Strategy Forum on Research Infrastructures
(ESFRI) since 2006. EMSO will specifically address long-term
monitoring of environmental processes related to marine ecosystems, marine mammals, climate change, and geohazards
A multicenter study on the appropriateness of hospitalization in obstetric wards: Application of Obstetric Appropriateness Evaluation Protocol (Obstetric AEP)
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