This work presents a range optimization of a SynRM (synchronous reluctance motor) and a PMaSynRM (permanent-magnet-assisted synchronous reluctance motor) according to a standard driving cycle, and the solutions obtained are compared. The proposed approach avoids the use of finite element analysis (FEA) during the optimization process, thus greatly reducing the time required to obtain the optimal solution. The paper validates the optimal motors obtained in different domains, since the methodology takes into account a multi-physics design. Using two coupled reluctance and thermal networks, all possible working points in the torque-speed plane are obtained taking into account thermal effects, magnetic saturation, iron losses as well as voltage and current constraints imposed by the inverter. The proposed approach allows a fast comparison of the solutions attained. The design and optimization methodology presented in this work can be applied to any driving cycle.
Este trabajo de investigación propone tres aportaciones principales en el campo de la previsión de consumos: la mejora en la exactitud de la predicción, la mejora en la adaptabilidad del modelo ante diferentes escenarios de consumo y la automatización en la ejecución de los algoritmos de modelado y predicción. La mejora de precisión que ha sido introducida en la estrategia de modelado propuesta ha sido obtenida tras la implementación de algoritmos de aprendizaje supervisados pertenecientes a las siguientes familias de técnicas: aprendizaje de máquinas, inteligencia computacional, redes evolutivas, sistemas expertos y técnicas de regresión.Otras las medidas implementadas para aumentar la calidad de la predicción han sido: la minimización del error de pronóstico a través de la extracción de información basada en análisis multi-variable, la combinación de modelos expertos especializados en atributos específicos del perfil de consumo, el uso de técnicas de pre procesamiento para aumentar la precisión a través de la limpieza de variables, y por último implementación de la algoritmos de clasificación no supervisados para obtener los atributos y las clases características del consumo.La mejora en la adaptación del algoritmo de modelado se ha conseguido mediante la implementación de tres componentes al interior de la estrategia de combinación de modelos expertos. El primer componente corresponde a la implementación de técnicas de muestreo sobre cada conjunto de datos agrupados por clase; esto asegura la replicación de la distribución de probabilidad global en múltiples y estadísticamente independientes subconjuntos de entrenamiento. Estos sub conjuntos son usados para entrenar los modelos expertos que consecuentemente pasaran a formar los modelos base de la estructura jerárquica que combina los modelos expertos.El segundo componente corresponde a técnicas de análisis multi-resolución. A través de la descomposición de variables endógenas en sus componentes tiempo-frecuencia, se abstraen e implementan conocimientos importantes sobre la forma de la estructura jerárquica que adoptaran los modelos expertos. El tercero componente corresponde a los algoritmos de modelado que generan una topología interior auto organizada, que proporciona de modelo experto base completamente personalizado al perfil de consumo analizado.La mejora en la automatización se alcanza mediante la combinación de procedimientos automáticos para minimizar la interacción de un usuario experto en el procedimiento de predicción. Los resultados experimentales obtenidos, a partir de la aplicación de las estrategias de predicción de consumos propuestas, han demostrado la idoneidad de las técnicas y metodologías implementadas; sobre todo en el caso de la novedosa estrategia para la combinación de modelos expertos.
This research work proposes three main contributions on the load forecasting field: the enhancement of the forecasting accuracy, the enhancement of the model adaptiveness, and the automatization on the execution of the load forecasting strategies implemented. On behalf the accuracy contribution, learning algorithms have been implemented on the basis of machine learning, computational intelligence, evolvable networks, expert systems, and regression approaches.
The options for increase the forecasting quality, through the minimization of the forecasting error and the exploitation of hidden insights and miscellaneous properties of the training data, are equally explored in the form of feature based specialized base learners inside of a modelling ensemble structure. Preprocessing and the knowledge discovery algorithms are also implemented in order to boost the accuracy trough cleaning of variables, and to enhance the autonomy of the modelling algorithm via non-supervised intelligent algorithms respectively.
The Adaptability feature has been enhanced by the implementation of three components inside of an ensemble learning strategy. The first one corresponds to resampling techniques, it ensures the replication of the global probability distribution on multiple independent training sub-sets and consequently the training of base learners on representatives spaces of occurrences.
The second one corresponds to multi-resolution and cyclical analysis techniques; through the decomposition of endogenous variables on their time-frequency components, major insights are acquired and applied on the definition of the ensemble structure layout. The third one corresponds to Self-organized modelling algorithms, which provides of fully customized base learner's.
The Autonomy feature is reached by the combination of automatic procedures in order to minimize the interaction of an expert user on the forecasting procedure. Experimental results obtained, from the application of the load forecasting strategies proposed, have demonstrated the suitability of the techniques and methodologies implemented, especially on the case of the novel ensemble learning strategy.
Este trabajo de investigación propone tres aportaciones principales en el campo de la previsión de consumos: la mejora en la exactitud de la predicción, la mejora en la adaptabilidad del modelo ante diferentes escenarios de consumo y la automatización en la ejecución de los algoritmos de modelado y predicción. La mejora de precisión que ha sido introducida en la estrategia de modelado propuesta ha sido obtenida tras la implementación de algoritmos de aprendizaje supervisados pertenecientes a las siguientes familias de técnicas: aprendizaje de máquinas, inteligencia computacional, redes evolutivas, sistemas expertos y técnicas de regresión. Otras las medidas implementadas para aumentar la calidad de la predicción han sido: la minimización del error de pronóstico a través de la extracción de información basada en análisis multi-variable, la combinación de modelos expertos especializados en atributos específicos del perfil de consumo, el uso de técnicas de pre procesamiento para aumentar la precisión a través de la limpieza de variables, y por último implementación de la algoritmos de clasificación no supervisados para obtener los atributos y las clases características del consumo. La mejora en la adaptación del algoritmo de modelado se ha conseguido mediante la implementación de tres componentes al interior de la estrategia de combinación de modelos expertos. El primer componente corresponde a la implementación de técnicas de muestreo sobre cada conjunto de datos agrupados por clase; esto asegura la replicación de la distribución de probabilidad global en múltiples y estadísticamente independientes subconjuntos de entrenamiento. Estos sub conjuntos son usados para entrenar los modelos expertos que consecuentemente pasaran a formar los modelos base de la estructura jerárquica que combina los modelos expertos. El segundo componente corresponde a técnicas de análisis multi-resolución. A través de la descomposición de variables endógenas en sus componentes tiempo-frecuencia, se abstraen e implementan conocimientos importantes sobre la forma de la estructura jerárquica que adoptaran los modelos expertos. El tercero componente corresponde a los algoritmos de modelado que generan una topología interior auto organizada, que proporciona de modelo experto base completamente personalizado al perfil de consumo analizado. La mejora en la automatización se alcanza mediante la combinación de procedimientos automáticos para minimizar la interacción de un usuario experto en el procedimiento de predicción. Los resultados experimentales obtenidos, a partir de la aplicación de las estrategias de predicción de consumos propuestas, han demostrado la idoneidad de las técnicas y metodologías implementadas; sobre todo en el caso de la novedosa estrategia para la combinación de modelos expertos.
Air gap eccentricity faults in five-phase ferrite-assisted synchronous reluctance motors (fPMa-SynRMs) tend to distort the magnetic flux in the air gap, which in turn affects the spectral content of both the stator currents and the ZSVC (zero-sequence voltage component). However, there is a lack of research works dealing with the topic of fault diagnosis in multi-phase PMa-SynRMs, and in particular, focused to detect eccentricity faults. The analysis of the spectral components of the line currents and the ZSVC, allows developing fault diagnosis algorithms to detect eccentricity faults. The effect of the operating conditions is also analyzed, since this paper shows that it has a non-negligible impact on the effectivity and sensitivity of the diagnosis based on the analysis of the stator currents and the ZSVC. To this end, different operating conditions are analyzed. The paper also evaluates the influence of the operating conditions on the harmonic content of the line currents and the ZSVC, and determines the most suitable operating conditions to enhance the sensitivity of the analyzed methods. Finally, fault indicators to detect eccentricity faults, which are based on the spectral content of the stator currents and the ZSVC are derived, and their performance is assessed. The approach presented in this work may be useful to develop fault diagnosis strategies based on the acquisition and subsequent analysis and interpretation of the spectral content of the line currents and the ZSVC.
Silicon carbide (SiC) devices provide significant performance improvements in many aspects, including lower
power dissipation, higher operating temperatures, and faster switching, compared to conventional Si devices. All these
features helped increase the interest in the applications of these devices for electric drive systems. The inclusion of an impedance network to elevate DC voltage would improve performance of an electric-traction system, because the topologies of impedances networks can eliminate the need of a DC-DC converter. However, it is important to know control methods that applicable to this type of topologies to systems that are more efficient. This paper presents the analysis of a control method in a power converter topology using SiC devices with an impedance network to elevate DC voltage for electric traction applications. The proposed analisys includes the implementation of a control method in Current Fed Quasi-Z topology, with 100 kHz switching frequency, and its analysis using the simulation of the control method, the power losses in SiC devices and the stress on passive components in the impedance network. Finally, the obtained results are compared with a conventional Current Fed Quasi-Z topology built with silicon devices at a low switching frequency (2 KHz).
Fernández, E.; Paredes, A.; Sala, V.; Romeral, L. International Conference on Power Engineering, Energy and Electrical Drives p. 464-470 DOI: 10.1109/CPE.2017.7915216 Data de presentació: 2017-04-06 Presentació treball a congrés
this paper presents the comparative analysis of power converters topologies with impedance source. The analysis consist in the comparison between two power converter topologies: the first topology is a current source inverter (CSI) that incorporates SiC devices with Quasi-Z source impedance; the second topology is CSI power converter built using SiC devices that incorporates Trans-Z source impedance. Both power converters operate with a switching frequency of 100 KHz. The use of SiC devices allows the converters to operate at higher frequency and to reduce the power losses. Additionally, it would allow the reduction of volume and weight of the elements of the source impedance. The goal of this article is to present an analysis of power losses, stress, volume and weight of the passive elements of the SiC proposed topologies using simulations and to compare them with conventional Si devices topologies with Quasi-Z and Trans-Z source impedance. The switching frequency in these topologies was set to 20 KHz in order to validate and demonstrate the advantages of SiC devices.
The operation of current source inverters at high frequency allows to reduce the size of the input coil and output filters. These advantages allowed to obtain converters with better performance and to reduce the size and weight of the passives elements, decreasing the manufacturing costs and while keeping the current harmonic distortion low. For these reasons, it is necessary to implement a modulation technique that allows the converter to work at a higher frequency 100 KHz.
This article presents the design and implementation of a Space Vector Modulation (SVM) technique for high switching frequencies for a Current Source Inverter (CSI) topology implemented with SiC devices. The technique is programmed and implemented in a PIC 24FJ256GA406 microcontroller, for the activation and control of SiC devices in each leg of power converter.
Paredes, A.; Sala, V.; Ghorbani, H.; Romeral, L. Annual Conference of the IEEE Industrial Electronics Society p. 3172-3177 DOI: 10.1109/IECON.2016.7793222 Data de presentació: 2017-02-24 Presentació treball a congrés
A novel active gate driver (AGD) for silicon carbide (SiC) MOSFET is studied in this paper. The gate driver (GD) increases the gate resistance value during the voltage plateau area of the gate-source voltage, in both turn-on and turn-off transitions. The proposed AGD is validated in both simulation and experimental environments and in hard-switching conditions. The simulation is evaluated in MATLAB/Simulink with 100 kHz of switching frequency and 600 V of dc-bus, whereas, the experimental part was realised at 100 kHz and 100 V of dc-bus. The results show that the gate driver can reduce the over-voltage and ringing, with low switching losses.
the development of different topologies of power converters with impedance networks have opened up new lines of research, its application in different areas such as transmission systems, high voltage, photovoltaic systems, these have yielded systems with high performance and efficiency, but in recent years its application in research and development of traction systems for electric vehicles have in creased, these topologies can be bidirectional and replace the DC-DC booster used in conventional systems. However, its implementation is necessary to
know the different modulation techniques and control that can be used to reach more efficient traction system and to consolidate these topologies. This article describes the different modulation and control techniques that can be applied to converter topologies with bidirectional impedance networks for applications in traction systems for electric vehicle.
Michalski, T.D.; Lopez, C.; Garcia, A.; Romeral, L. Annual Conference of the IEEE Industrial Electronics Society p. 1-6 DOI: 10.1109/IECON.2016.7793740 Data de presentació: 2016-10-27 Presentació treball a congrés
This paper deals with the realization of a sensorless five phase permanent magnet synchronous motor (PMSM) drive based on extended Kalman filter (EKF). The vector control for five phase AC machine is applied on dq1dq3 rotor reference planes and structure of the observer is extracted on the fixed stator reference frames of the first and third harmonics. The zero sequence component may also be incorporated in the state vector in order to facilitate fault tolerant operation.
Michalski, T.D.; Lopez, C.; Garcia, A.; Romeral, L. Annual Conference of the IEEE Industrial Electronics Society p. 1-6 DOI: 10.1109/IECON.2016.7793256 Data de presentació: 2016-10-26 Presentació treball a congrés
Sala, E.; Zurita, D.; Kampouropoulos, K.; Delgado Prieto, M.; Romeral, L. Annual Conference of the IEEE Industrial Electronics Society p. 4002-4007 DOI: 10.1109/IECON.2016.7793491 Data de presentació: 2016-10-26 Presentació treball a congrés
Buildings often operate under inefficient conditions and configurations due to their complex dynamics, a necessity of in-depth knowledge and intricate analysis tools. The fact is that interest in proposing higher order approaches for tackling efficiency problems in buildings has been steadily increasing during recent years. A wide range of approaches are being demonstrated, from model-predictive control schemes to frameworks for the detection of anomalies in energy consumption. Occupancy-centric methodologies, in particular, present one of the avenues with greatest potential of improvement because of their ability to adapt the behavior of the building to the real necessities of the users. This paper presents a novel occupancy modeling and forecasting methodology with the capability to support downstream demand-side management tools by providing accurate insight regarding the occupancy of spaces in the building. The proposed methodology takes advantage of the availability of presence detectors located on the different spaces of the building to study their dynamics and autonomously map their behavior. The complete methodology is validated experimentally in terms of accuracy and performance using real data from a research building.
Kampouropoulos, K.; Andrade, F.; Sala, E.; Garcia, A.; Romeral, L. Annual Conference of the IEEE Industrial Electronics Society p. 3990-3995 DOI: 10.1109/IECON.2016.7793570 Data de presentació: 2016-10-26 Presentació treball a congrés
Due to the climate change and the decrease in fossil fuel reserves, the industrial and tertiary sectors have been focused on the implementation of advanced energy management systems in order to improve their energy efficiency and reduce their overall emissions. One way to achieve that goal, which is also the focus of this work, is by optimizing the energy use in their operation processes. This paper presents a hybrid optimization method, combined by neuro-fuzzy inference systems and the quadratic programming optimization method, to calculate the short-term demand forecasting of a multi-carrier energy system and to optimize its energy flow. The objective of the optimization is to fulfill the system's energy demands and minimize a set of established optimization criteria. Moreover, the algorithm considers the system's dynamics and inertias in order to guarantee that the obtained results present a feasible and stable operation strategy for the energetic plant. The method has been applied and validated under real conditions in a car manufacturing plant of Spain, in the framework of a FP7 European research project using online production and consumption data.
Sala, E.; Kampouropoulos, K.; Delgado Prieto, M.; Romeral, L. Annual Conference of the IEEE Industrial Electronics Society p. 3978-3983 DOI: 10.1109/IECON.2016.7794112 Data de presentació: 2016-10-26 Presentació treball a congrés
The increasing ubiquity of sensing and metering devices in buildings is a gateway of opportunities for the analysis of their behavior and the detection of anomalies or sub-optimal performance. In particular, the instrumentation of HVAC equipment, necessary for its monitoring and control, may be used in order to supervise its operation at a finer level. The intelligent supervision methodology proposed in this paper allows the accurate overseeing of the power consumption of HVAC equipment by means of establishing the relationship between the power consumed and the operating status of the installation, and its individual machines. The accurate correlation between the instantaneous power and the available control or state signals of the equipment, simultaneously with other support variables, allows detecting malfunctions or deviations from their nominal operation. First, a model of the power demand of the installation is obtained by means of a training function. Afterwards, the model can be applied in real-time over new samples in order to check if the power demand corresponds to the state of operation of the installation. In addition to the accurate tracking of the power demand, the chosen approach allows to monitor the installation with a single power meter, therefore decreasing the cost of implementation. Finally, this methodology has been validated by means of experimental data from a pilot plant where the complete system has been implemented.
This paper proposes a methodology for optimal design of Permanent Magnet assisted Synchronous Reluctance Motor. The magnetic model is explained because the particularities associated to high magnetic saturation regions this motor has, which lead to difficulties in inductance calculation. Hence, the finite element analysis is currently used to design and optimize Permanent Magnet assisted Synchronous Reluctance Motor, from the first. This method requires a high amount of computational time and resources. For this reason, the magnetic model explained in this paper is a good alternative to introduce on optimal design process. The magnetic model proposed is based on reluctance network and takes into account the magnet effects and the possibility of structural ribs on motor design.
Ghorbani, H.; Sala, V.; Paredes, A.; Romeral, L. Annual Conference of the IEEE Industrial Electronics Society p. 3547-3551 DOI: 10.1109/IECON.2016.7793741 Data de presentació: 2016-10-24 Presentació treball a congrés
This paper presents a new active gate control (AGC) approach for improving the switching behavior of insulated gate bipolar transistors (IGBTs). The proposed controller is applied on the gate driver (GD) of IGBT, which is based on Posicast control method. The reduction of stress in transient conditions without harmful effect on the efficiency is the main objective of this research that is accomplished by a simple feed-forward controller.
The effectiveness of the proposed gate drive controller is verified by both MATLAB/Simulink and PSIM softwares. Moreover, the new GD is implemented in the experimental setup, an
d the results are reflected in this paper.
Fernández, E.; Paredes, A.; Romeral, L.; Sala, V. International Power Electronics and Motion Control Conference p. 267-272 DOI: 10.1109/EPEPEMC.2016.7752009 Data de presentació: 2016-09-27 Presentació treball a congrés
This article presents the analysis of two topologies
of power converters. Voltage Source Inverter (VSI) and Current
Source Inverter (CSI) proposals for traction system applications,
these topologies are implemented with silicon carbide devices.
The use of SiC semiconductors allow working at high switching
frequency (100KHz), increase the working temperature range
and decreasing power losses during conduction and activation of
The objective is analyze these topologies and select the one that
provides the best performance and behavior at high frequency
to improve it on a electric traction system.
Kampouropoulos, K.; Andrade, F.; Sala, E.; Garcia, A.; Romeral, L. IEEE Transactions on Smart Grid Vol. PP, num. 99, p. 1-9 DOI: 10.1109/TSG.2016.2609740 Data de publicació: 2016-09-14 Article en revista
This paper presents a novel method for the energy optimization of multi-carrier energy systems. The presented method combines an adaptive neuro-fuzzy inference system, to model and forecast the power demand of a plant, and a genetic algorithm to optimize its energy flow taking into account the dynamics of the system and the equipment’s thermal inertias. The objective of the optimization algorithm is to satisfy the total power demand of the plant and to minimize a set of optimization criteria, formulated as energy usage, monetary cost and environmental cost. The presented method has been validated under real conditions in the car manufacturing plant of SEAT in Spain in the framework of an FP7 European research project.
Ghorbani, H.; Sala, V.; Paredes, A.; Romeral, L. European Conference on Power Electronics and Applications p. 1-7 DOI: 10.1109/EPE.2016.7695436 Data de presentació: 2016-09-07 Presentació treball a congrés
This paper proposes a novel insulated gate bipolar transistor (IGBT) gate driver. The new gate driver
(GD) has positive effect on the injected gate current to enhance the IGBT switching mechanism. The
approach is based on the Posicast control method. The simple structure is the most important advantage of
this feedforward controller. The main objective is to improve turn-on switching transients without
harmful effect on the IGBT efficiency. The electromagnetic interface (EMI) reduction has been
discussed as another important benefit of this control method.
Lopez, C.; Michalski, T.D.; Garcia, A.; Romeral, L. International Conference on Electrical Machines p. 2052-2058 DOI: 10.1109/ICELMACH.2016.7732805 Data de presentació: 2016-09-06 Presentació treball a congrés
An optimal design for a rotor of SynRM is proposed on this paper. The inductances of the machine computed in dq-axes allow determining machine performance and the motor behavior. High magnetic saturation on this kind of motor increases the difficulty of inductance calculation. Hence, the finite element analysis is currently used to design and optimize SynRM, from the first instance. This method usually requires a high amount of computational time and resources. For this reason, the reluctance network here proposed is a good alternative to consider for designing these motors, because it is a fast and good method to obtain the inductances of the SynRM. Therefore, an optimal design avoiding FEA is proposed on this paper using the reluctance network to calculate the dq-axes motor inductances.
Lopez, C.; Michalski, T.D.; Garcia, A.; Romeral, L. International Conference on Electrical Machines p. 1915-1921 DOI: 10.1109/ICELMACH.2016.7732785 Data de presentació: 2016-09-05 Presentació treball a congrés
A novel motor design methodology and optimization is proposed, which is applied to a SRM. It greatly reduces the finite element analysis use. A multi physics study is realized in order to couple the thermal, magnetic and electric motor analysis. Then, the information obtained with this study allows creating different behaviors maps in order to evaluate the motor in all operating area. With the joule and iron losses, the thermal network obtains a temperature map for each current and angular speed. Then, the electric model uses the parameters obtained on reluctances network and the phase resistance variation obtained on thermal lumped parameter. Finally, the optimal motor obtained is validated on FEA. To sum up, the motor design is obtained using multi physics analysis taking into account different operating points, i.e. it is a range operation optimization. In addition, reducing FEA computation time is achieved with the methodology proposed.
Sala, E.; Kampouropoulos, K.; Delgado Prieto, M.; Romeral, L. IEEE International Conference on Emerging Technologies and Factory Automation p. 1-6 DOI: 10.1109/ETFA.2016.7733568 Data de presentació: 2016-09 Presentació treball a congrés
This paper presents a load disaggregation method for the monitoring and supervision of the load profiles
of individual equipment in an HVAC installation. The method takes advantage of the wealth of sensor and actuation
information found in Building Energy Management Systems in order to find correlations between the state of operation
of each machine and the power demand of the installation. This enables to model the individual power of the
equipment on account of their state, and in combination with other support variables that influence their load demand,
such as weather conditions. The resulting array of equipment models can be evaluated in real-time to infer the
expected power consumption of each machine. Then, allowing the tracking of their individual power consumption while
at the same time significantly lowering the cost of the acquisition and monitoring infrastructure, because a single power
meter can be used to accurately monitor several machines when following this approach. The presented method has
been validated by means of experimental data from a pilot plant where the complete system has been implemented.
Giacometto, Francisco javier; Capelli, F.; Romeral, L.; Riba, J.; Sala, E. Advances in Electrical and Computer Engineering Vol. 16, num. 3, p. 25-30 DOI: 10.4316/AECE.2016.03004 Data de publicació: 2016-08-15 Article en revista
Wang, C.; Delgado Prieto, M.; Romeral, L.; Chen, Z.; Blaabjerg, F.; Liu, X. IEEE transactions on magnetics Vol. 52, num. 7 DOI: 10.1109/TMAG.2015.2511003 Data de publicació: 2016-07-01 Article en revista
Demagnetization fault detection of in-service permanent magnet synchronous machines (PMSMs) is a challenging task, because most PMSMs operate under nonstationary circumstances in industrial applications. A novel approach based on tracking characteristic orders of stator current using Vold-Kalman filter is proposed to detect the partial demagnetization fault in PMSMs running at nonstationary conditions. The amplitude of envelope of the fault characteristic orders is used as fault indictor. Experimental results verify the superiority of the proposed method on the partial demagnetization online fault detection of PMSMs under various speed and load conditions.
Several factors including fossil fuels scarcity, prices volatility, greenhouse gas emissions or current pollution levels in metropolitan areas are forcing the development of greener transportation systems based on more efficient electric and hybrid vehicles. Most of the current hybrid electric vehicles use electric motors containing powerful rare-earth permanent magnets. However, both private companies and estates are aware of possible future shortages, price uncertainty and geographical concentration of some critical rare-earth elements needed to manufacture such magnets. Therefore, there is a growing interest in developing electric motors for vehicular propulsion systems without rare-earth permanent magnets. In this paper this problematic is addressed and the state-of-the-art of the electric motor technologies for vehicular propulsion systems is reviewed, where the features required, design considerations and restrictions are addressed.
The use of impedance networks in different types of DC/DC, DC/AC and AC/AC converters, has increased significantly, and many converters topologies and articles VSI and CSI with impedance networks have been presented for the purpose of overcoming the limitations and problems of voltage and current that frequently occurs in these topologies. The selection and implementation of a topology of network impedance would improve the reliability and performance of the power system. This article presents a study and analysis of different network impedance topologies, the modulation techniques and control for the adapting to power converters for applications in electric traction.
the traction systems for electric vehicles have advanced considerably over recent years, with the application of different topologies of power converters for the control of various types of electric induction motors and permanent magnet. Furthermore, the evolution of power semiconductor elements of Si to SiC wideband have opened up lines of research and development in this area. The trend of manufacturer's traction systems is to reach compact systems where the power dissipation is high and the reduction of losses is minimal, for it the implementation of topologies of converters with SiC devices seems to be a good alternative of use to improve the performance of these systems; This paper describes a study and review of the different types of converter topologies proposed for the development and application in traction systems for electric vehicles. This review will identify the different works presented and analyze their problems, with the aim of seek to optimize these topologies or propose new types of topologies for implementation in traction systems.
Delgado Prieto, M.; Zurita, D.; Wang, W.; Machado, A.; Ortega, J.A.; Romeral, L. IEEE transactions on instrumentation and measurement Vol. 65, num. 1, p. 15-24 DOI: 10.1109/TIM.2015.2476278 Data de publicació: 2016-01-01 Article en revista
Advanced sensing strategies in the industrial sector are becoming a valued technological answer to increase the performance and competitiveness. The development of enhanced sensing solutions considering both technology and monitoring requirements is, nowadays, subject of concern in the industrial maintenance field. In this context, this paper presents a novel self-powered wireless sensor applied to condition monitoring of gears. The proposed sensor is based on a modular architecture, offering multipoint sensing, local wireless communication, multisource energy harvesting, and embedded diagnosis algorithm for mechanical fractures detection based on acoustic emission analysis. The developments are complemented by means of a remote management interface, from which the user can configure the functionalities of the sensors, visualize the network status as well as analyze the diagnosis evolution. The sensor performance, in terms of power consumption and fault detection, has been analyzed by means of experimental results.