Scientific publications

The high quality research carried out in the Center throughout PhD theses is disseminated by means of national and international journals and conferences. 

In FUTUR UPC a complete and updated list of the Center’s Theses and Publications can be found.

As a technological center located in the University Campus, the MCIA Center shows an intense and dynamic research branch. Part of the MCIA staff corresponds to Master Engineers and PhD students. This fact makes possible the research towards novel contributions in each of the MCIA's areas, at the same time that the latest validated developments are applied in technology transfer projects.




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. +info

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. +info 

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


The MCIA Center accumulates a considerable experience in the supervision of doctoral's theses as result of the continuous research activity. Currently, more than 10 theses are in progress.

Lastest theses presented:

High Efficiency Sensorless Fault Tolerant Control of Permanent Magnet Assisted Synchronous Reluctance Motor

Michalski, Tomasz Dobromir

Supervisors: Romeral Martinez, Jose Luis

Jury: President: Muñoz Hernández, Germán Ardul. Secretary: Moreno Eguilaz, Juan Manuel. Vocal: Rolak, Michal.

Date: October 2021

Overview: In the last decades, the development trends of compact and high-efficiency electric drives on the motor side have focused on permanent magnet synchronous machines (PMSM) equipped with magnets based on rare earth elements. However, permanent magnet components dramatically impact motor construction cost. This aspect has become even more critical due to price volatility for rare earth elements. This is why the permanent magnet assisted synchronous reluctance motor (PMaSynRM) concept has been taken into consideration as it offers comparable torque density and efficiencies similar to PMSM, albeit at a lower price credited for the use of magnets made of ferrite compounds. Although PMaSynRM drive is very complex due to non-linear inductances resulting from deep cross saturation effects, this is also true for polyphase PMSM motors that have gained a lot of attention in recent years, in which the power is proportionally divided by the largest number of phases. Also, they offer fault-tolerant operation while one or more phases are idle due to machine, inverter, or sensor faults. However, the number of phases further increases the overall complexity of the modeling and control design. It is clear then that a combination of multiphase with the PMaSynRM concept has potential benefits, but hinders standard modeling methods and drive system development techniques.This thesis consists of detailed modeling, control design and implementation of a five phase PMaSynRM drive for normal healthy and fault tolerant open phase applications. +Info

Development of the future generation of smart high voltage connectors and related components for substations, with energy autonomy and wireless data transmission capability

Kadechkar, Akash

Supervisors: Riba Ruiz, Jordi Roger; Moreno Eguilaz, Juan Manuel

Jury: President: Pallarès Marzal, Josep. Secretary: Ortega Redondo, Juan Antonio. Vocal: Jordà I sunuy, Xavier.

Date: October 2020

Overview: The increased dependence on electricity in modern society makes the reliability of power transmission systems a key point. This objective can be achieved by continuously monitoring the parameters of the electrical network, whereby possible failure modes can be predicted in advance. It can be done using existing Information and Communication Technologies (1CT) and Internet of Things (loT) technologies that include instrumentation and wireless communication systems, thus forming a wireless sensor network (WSN). Electrical connectors are among the most critical parts of any electrical system and therefore can act as nodes of such a VVSN. Therefore, the fundamental objective of this thesis is the design, development and experimental validation of a self-powered IOT solution for real-time monitoring of the health status of a high-voltage substation connector and related components of the electrical substation. This new family of power connectors is called SmartConnector and incorporates a thermal energy harvesting system that powers a microcontroller that controls a transmitter and various electronic sensors to measure the connector's temperature, current, and electrical contact resistance (ECR). +Info

Active Gate Drivers for High-Frequency Application of SiC MOSFETs

Paredes Camacho, Alejandro

Supervisors: José Luis Romeral & Vicente Sala Casellas

Jury: President: Perpiñà Giribert, Xavier. Secretary: Moreno Eguilaz, Juan Manuel. Vocal: Saavedra Ordoñez, Harold.

Date: July 2020

Overview: The trend in the development of power converters is focused on efficient systems with high power density, reliability and low cost. The challenges to cover the new power converters requirements are mainly concentered on the use of new switching-device technologies such as silicon carbide MOSFETs (SiC). SiC MOSFETs have better characteristics than their silicon counterparts; they have low conduction resistance, can work at higher switching speeds and can operate at higher temperature and voltage levels. Despite the advantages of SiC transistors, operating at high switching frequencies, with these devices, reveal new challenges. The fast switching speeds of SiC MOSFETs can cause over-voltages and over-currents that lead to electromagnetic interference (EMI) problems. For this reason, gate drivers (GD) development is a fundamental stage in SiC MOSFETs circuitry design. The reduction of the problems at high switching frequencies, thus increasing their performance, will allow to take advantage of these devices and achieve more efficient and high power density systems. This Thesis consists of a study, design and development of active gate drivers (AGDs) aimed to improve the switching performance of SiC MOSFETs applied to high-frequency power converters. Every developed stage regarding the GDs is validated through tests and experimental studies. In addition, the developed GDs are applied to converters for wireless charging systems of electric vehicle batteries. The results show the effectiveness of the proposed GDs and their viability in power converters based on SiC MOSFET devices. +Info