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Cecati C.,University of LAquila | Cecati C.,DigiPower Ltd. | Kolbusz J.,Rzeszow University of Technology | Rozycki P.,Rzeszow University of Technology | And 2 more authors.
IEEE Transactions on Industrial Electronics | Year: 2015

Because of their excellent scheduling capabilities, artificial neural networks (ANNs) are becoming popular in short-term electric power system forecasting, which is essential for ensuring both efficient and reliable operations and full exploitation of electrical energy trading as well. For such a reason, this paper investigates the effectiveness of some of the newest designed algorithms in machine learning to train typical radial basis function (RBF) networks for 24-h electric load forecasting: support vector regression (SVR), extreme learning machines (ELMs), decay RBF neural networks (DRNNs), improves second order, and error correction, drawing some conclusions useful for practical implementations. © 2015 IEEE. Source


Barbati M.,University of LAquila | Caluisi C.,University of LAquila | Cecati C.,DigiPower Ltd.
IECON Proceedings (Industrial Electronics Conference) | Year: 2010

This paper presents a discrete implementation of a Triangular One Cycle Control for three phase active rectifiers. The algorithm is characterised by triangular modulation carrier and can be implementated using a programmable logic device, thus obtaining increased feasibility and performance over analog OCCs systems. Simulation results and comparisons with another three-phase active rectifier control method show that the proposed MT-OCC offers satisfactory performance in terms of high power factor control, low harmonic pollution, fast and accurate dc output voltage regulation and high stability during regenerative operations. © 2010 IEEE. Source


Gungor V.C.,Bahcesehir University | Sahin D.,Bahcesehir University | Kocak T.,Bahcesehir University | Ergut S.,Turk Telekom Group RandD Division | And 5 more authors.
IEEE Transactions on Industrial Informatics | Year: 2011

For 100 years, there has been no change in the basic structure of the electrical power grid. Experiences have shown that the hierarchical, centrally controlled grid of the 20th Century is ill-suited to the needs of the 21st Century. To address the challenges of the existing power grid, the new concept of smart grid has emerged. The smart grid can be considered as a modern electric power grid infrastructure for enhanced efficiency and reliability through automated control, high-power converters, modern communications infrastructure, sensing and metering technologies, and modern energy management techniques based on the optimization of demand, energy and network availability, and so on. While current power systems are based on a solid information and communication infrastructure, the new smart grid needs a different and much more complex one, as its dimension is much larger. This paper addresses critical issues on smart grid technologies primarily in terms of information and communication technology (ICT) issues and opportunities. The main objective of this paper is to provide a contemporary look at the current state of the art in smart grid communications as well as to discuss the still-open research issues in this field. It is expected that this paper will provide a better understanding of the technologies, potential advantages and research challenges of the smart grid and provoke interest among the research community to further explore this promising research area. © 2011 IEEE. Source


Cecati C.,University of LAquila | Cecati C.,DigiPower Ltd. | Ciancetta F.,University of Salerno | Siano P.,University of LAquila
IEEE Transactions on Industrial Electronics | Year: 2010

Converters for photovoltaic (PV) systems usually consist of two stages: a dc/dc booster and a pulsewidth modulated (PWM) inverter. This cascade of converters presents efficiency issues, interactions between its stages, and problems with the maximum power point tracking. Therefore, only part of the produced electrical energy is utilized. In this paper, the authors propose a single-phase H-bridge multilevel converter for PV systems governed by a new integrated fuzzy logic controller (FLC)/modulator. The novelties of the proposed system are the use of a fully FLC (not requiring any optimal PWM switching-angle generator and proportionalintegral controller) and the use of an H-bridge power-sharing algorithm. Most of the required signal processing is performed by a mixed-mode field-programmable gate array, resulting in a fully integrated System-on-Chip controller. The general architecture of the system and its main performance in a large spectrum of practical situations are presented and discussed. The proposed system offers improved performance over two-level inverters, particularly at lowmedium power. © 2010 IEEE. Source


Strasser T.,AIT Austrian Institute of Technology | Andren F.,AIT Austrian Institute of Technology | Kathan J.,AIT Austrian Institute of Technology | Cecati C.,University of LAquila | And 12 more authors.
IEEE Transactions on Industrial Electronics | Year: 2015

Renewable energy sources are one key enabler to decrease greenhouse gas emissions and to cope with the anthropogenic climate change. Their intermittent behavior and limited storage capabilities present a new challenge to power system operators to maintain power quality and reliability. Additional technical complexity arises from the large number of small distributed generation units and their allocation within the power system. Market liberalization and changing regulatory framework lead to additional organizational complexity. As a result, the design and operation of the future electric energy system have to be redefined. Sophisticated information and communication architectures, automation concepts, and control approaches are necessary in order to manage the higher complexity of so-called smart grids. This paper provides an overview of the state of the art and recent developments enabling higher intelligence in future smart grids. The integration of renewable sources and storage systems into the power grids is analyzed. Energy management and demand response methods and important automation paradigms and domain standards are also reviewed. © 1982-2012 IEEE. Source

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