Antonio Caño

Universidad de Jaén

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Francisco Jurado

Universidad de Jaén

Paúl Arévalo

Universidad de Jaén

F. Sánchez-Sutil

Universidad de Jaén

Darío Benavides

Universidad de Jaén

Jesús C. Hernández

Universidad de Jaén

Luis M. Fernández‐Ramírez

United States Naval Research Laboratory

Higinio Sánchez–Sainz

Universidad de Cádiz

Manuel Ortega

Universidad de Jaén

Mohamed R. Gomaa

Benha University

Edisson Villa‐Ávila

Universidad de Jaén

Cooperative Institutions

Universidad de Jaén

Universidad de Cádiz

University of Campania "Luigi Vanvitelli"

Universidad Politécnica de Madrid

Malaviya National Institute of Technology Jaipur

Gebze Technical University

University of Cuenca

University of Patras

Bilecik University

Indian Institute of Management Lucknow

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Sustainable tramway, techno-economic analysis and environmental effects in an urban public transport. A comparative study

Sustainable Energy Grids and Networks (2021)

Antonio Caño Paúl Arévalo Darío Benavides Francisco Jurado

Regenerative brake

10.1016/j.segan.2021.100462

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Citations (17)

Enhancing the distribution system stability using micro-turbines and fuel cells

Francisco Jurado Antonio Caño José Manuel Murillo Carpio

Introducing high concentrations of distributed power generation units into existing electrical networks can compromise the dynamic behavior of the distribution system as a whole. The two candidate technologies for distributed generation that are arousing the most excitement right now are fuel cells and micro-turbines. What has made the concept of distributed power especially attractive is, of course, all the innovative electronics that has lowered the cost of protective relaying, improved remote control, and simplified interfaces between generating resources and the grid. This paper describes models of micro-turbines and fuel cells, which can be used in stability studies. Examinations include transient stability and voltage stability of the system.

Transient (computer programming)

Power Electronics

10.1109/tdc.2003.1335364

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Citations (7)

Modeling micro-turbines on the distribution system using identification algorithms

Antonio Caño Francisco Jurado

To determine the potential impacts of micro-turbines on future distribution system, dynamic models of micro-turbines should be created, reduced in order, and scattered throughout test feeders. This paper presents the implementation of an efficient method for computing low order linear system models of micro-turbines from time domain simulations. The method is the Box-Jenkins algorithm for calculating the transfer function of a linear system from samples of its input and output.

Identification

10.1109/etfa.2003.1248769

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Citations (6)

A Platform for Analysing Huge Amounts of Data from Households, Photovoltaics, and Electrical Vehicles: From Data to Information

Electronics (2022)

Antonio Caño Miguel Ángel García Cumbreras F. Sánchez-Sutil Jesús C. Hernández

Analytics is an essential procedure to acquire knowledge and support applications for determining electricity consumption in smart homes. Electricity variables measured by the smart meter (SM) produce a significant amount of data on consumers, making the data sets very sizable and the analytics complex. Data mining and emerging cloud computing technologies make collecting, processing, and analysing the so-called big data possible. The monitoring and visualization of information aid in personalizing applications that benefit both homeowners and researchers in analysing consumer profiles. This paper presents a smart meter for household (SMH) to obtain load profiles and a new platform that allows the innovative analysis of captured Internet of Things data from smart homes, photovoltaics, and electrical vehicles. We propose the use of cloud systems to enable data-based services and address the challenges of complexities and resource demands for online and offline data processing, storage, and classification analysis. The requirements and system design components are discussed.

Smart meter

Data Analysis

Smart City

Data Processing

10.3390/electronics11233991

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Citations (5)

Wood gasification integrated with fuel cell-microturbine hybrid power plant

Francisco Jurado Antonio Caño N. Acero M. Ortega M. Valverde

A system showing great promise is integration of gasification with a fuel cell. The fuel cell also produces very high-temperature exhaust gases that can either be used directly in combined-cycle applications or used to drive a gas turbine. Fuel cell-microturbine combination has the potential to achieve up to 60 percent efficiency and near-zero emissions. Fuel flexibility enables the use of low-cost indigenous fuels, renewables and waste materials. The characteristics of gas from biomass gasification may vary significantly. Traditional control design approaches consider a fixed operating point in the hope that the resulting controller is robust enough to stabilize the system for different operating conditions. On the other hand, robust control incorporates the uncertain parameters of the model.

10.1109/icpst.2002.1047224

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Integrating discrete wavelet transform with neural networks and machine learning for fault detection in microgrids

International Journal of Electrical Power & Energy Systems (2023)

Antonio Caño Paúl Arévalo Darío Benavides Francisco Jurado

Microgrids are essential for integrating renewable energy sources into the power grid. However, fault detection is challenging due to bidirectional energy flow. Traditional relay-based systems struggle in microgrids, primarily because of limited fault currents from grid-connected renewable energy inverters. To address these challenges, this paper proposes a new methodology for fault detection and classification in a renewable microgrid. The main contributions encompass two key aspects. Firstly, it enhances fault detection performance in microgrids characterized by nonlinear relationships, including photovoltaic, hydrokinetic, and variable electric load systems. Secondly, the combination of the discrete wavelet transform with various types of neural networks and supervised learning techniques provides a robust methodology for fault detection and classification. The proposed approach is evaluated using an IEEE-5 feeder test bed representing a realistic ring network configuration. The results show that the radial basis function neural network model exhibited promising outcomes, yielding a low prediction error of 1.31 e-31, highlighting its practical potential for enhancing system reliability and performance. Furthermore, various test cases were conducted by altering the ground resistance to train the neural networks, demonstrating the effectiveness of this neural network in accurately identifying fault conditions. Additionally, this research achieved promising outcomes with other models, including support vector machine and nonlinear autoregressive with external input, emphasizing the adaptability of these models in fault detection.

Microgrid

10.1016/j.ijepes.2023.109616

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Citations (18)

Influence of micro-turbines on distribution networks stability

2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491) (2004)

Antonio Caño Francisco Jurado J. Carpio

Micro-turbine is an electric power supply alternative that is located closer to end-use equipment. However, there are a number of regulatory and technical barriers that must be overcome before large-scale use of micro-turbine can take place. This paper describes a dynamic model of a micro-turbine, which can be used in stability studies. The model is applied to a distributed utility grid that uses a micro-turbine plant as distributed resource. Transient stability and voltage stability of the system are investigated.

10.1109/pes.2003.1270949

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Citations (12)

Neuro-fuzzy controller for gas turbine in biomass-based electric power plant

Electric Power Systems Research (2002)

Francisco Jurado Manuel Ortega Antonio Caño José Carpio

10.1016/s0378-7796(01)00187-0

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Citations (74)

List of contributors

Elsevier eBooks (2024)

Sameh O. Abdellatif José A. Aguado Jacobo Aguillón-García Dennis Albert Bora Alboyacı

10.1016/b978-0-443-21644-2.00025-7

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Smart Control and Energy Efficiency in Irrigation Systems Using LoRaWAN

Sensors (2021)

F. Sánchez-Sutil Antonio Caño

Irrigation installations in cities or agricultural operations use large amounts of water and electrical energy in their activity. Therefore, optimising these resources is essential nowadays. Wireless networks offer ideal support for such applications. The long-range wide-area network (LoRaWAN) used in this research offers a large coverage of up to 5 km, has low power consumption and does not need additional hardware such as repeaters or signal amplifiers. This research develops a control and monitoring system for irrigation systems. For this purpose, an irrigation algorithm is designed that uses rainfall probability data to regulate the irrigation of the installation. The algorithm is complemented by checking the sending and receiving of information in the LoRa network to reduce the loss of information packets. In addition, two temperature and humidity measurement devices for LoRaWAN (THMDLs) and an electrovalve control device for LoRaWAN (ECDLs) were developed. The hardware and software were also designed, and prototypes were built with the development of the electronic board. The wide coverage of the LoRaWAN allows the covering of small to large irrigation areas.

10.3390/s21217041

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Citations (27)