Publications

Some of the latest contributions to international congresses and international journals are listed next. The complete list of publications can be found here.

Black-box modeling of DC-DC converters based on wavelet convolutional neural networks

Authors: 

Rojas, G.; Riba, J.; Moreno-Eguilaz, J.M.

Journal: 

IEEE trnsactions on instrumentation and measurement, July 2021

Abstract:

This paper presents an offline deep learning approach focused to model and identify a 270 V-to-28 V DC-DC step-down converter used in on-board distribution systems of more electric aircrafts (MEA). Manufacturers usually do not provide enough information of the converters. Thus, it is difficult to perform design and planning tasks and to check the behavior of the power distribution system without an accurate model. This work considers the converter as a black-box, and trains a wavelet convolutional neural network (WCNN) that is able of accurately reproducing the behavior of the DC-DC converter from a large set of experimental data. The methodology to design a WCNN based on the characteristics of the input and output signals of the converter is also described. The method is validated with experimental data obtained from a setup that replicates the 28 V on-board distribution system of an aircraft. The results presented in this paper show a high correlation between measured and estimated data, robustness and low computational burden. This paper also compares the proposed approach against other techniques presented in the literature. It is possible to extend this method to other DC-DC converters, depending on their requirements.

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Autonomous energy management system with self-healing capabilities for green buildings (Microgrids)

Authors: 

Selseleh Jonban, M.; Romeral, L.; Akbarimajd, A.; Ali, Z.; Ghazimirsaeid, S.; Marzband, M.; Putrus , G

Journal: 

Journal of building engineering, Feb 2021

Abstract:

Nowadays, distributed energy resources are widely used to supply demand in micro grids specially in green buildings. These resources are usually connected by using power electronic converters, which act as actuators, to the system and make it possible to inject desired active and reactive power, as determined by smart controllers. The overall performance of a converter in such system depends on the stability and robustness of the control techniques. This paper presents a smart control and energy management of a DC microgrid that split the demand among several generators. In this research, an energy management system (EMS) based on multi-agent system (MAS) controllers is developed to manage energy, control the voltage and create balance between supply and demand in the system with the aim of supporting the reliability characteristic. In the proposed approach, a reconfigurated hierarchical algorithm is implemented to control interaction of agents, where a CAN bus is used to provide communication among them. This framework has ability to control system, even if a failure appears into decision unit. Theoretical analysis and simulation results for a practical model demonstrate that the proposed technique provides a robust and stable control of a microgrid.

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 A Data-Driven-Based Industrial Refrigeration Optimization Method Considering Demand Forecasting

Authors: 

J. Cirera; J. A. Carino; D. Zurita; and J. A. Ortega

Journal: 

Processes, May 2020

Abstract:

One of the main concerns of industry is energy efficiency, in which the paradigm of Industry 4.0 opens new possibilities by facing optimization approaches using data-driven methodologies. In this regard, increasing the efficiency of industrial refrigeration systems is an important challenge, since this type of process consume a huge amount of electricity that can be reduced with an optimal compressor configuration. In this paper, a novel data-driven methodology is presented, which employs self-organizing maps (SOM) and multi-layer perceptron (MLP) to deal with the (PLR) issue of refrigeration systems. The proposed methodology takes into account the variables that influence the system performance to develop a discrete model of the operating conditions. The aforementioned model is used to find the best PLR of the compressors for each operating condition of the system. Furthermore, to overcome the limitations of the historical performance, various scenarios are artificially created to find near-optimal PLR setpoints in each operation condition. Finally, the proposed method employs a forecasting strategy to manage the compressor switching situations. Thus, undesirable starts and stops of the machine are avoided, preserving its remaining useful life and being more efficient. An experimental validation in a real industrial system is performed in order to validate the suitability and the performance of the methodology. The proposed methodology improves refrigeration system efficiency up to 8%, depending on the operating conditions. The results obtained validates the feasibility of applying data-driven techniques for the optimal control of refrigeration system compressors to increase its efficiency. +info