This paper presents a gate-driving strategy to address high-speed switching problems in Gallium Nitride (GaN) transistors. The strategy consists of emulating a multi-stage gate-voltage (VG) profile by applying ultra-fast pulse sequences (UFPS) embedded within a conventional PWM signal and applied to GaN transistors. The implementation is achieved using a field-programmable gate array (FPGA) and validated on a GaN device under test (DUT) through experimental testing. The results confirm the feasibility of the approach and its effectiveness in mitigating high-speed switching issues associated with GaN transistors.

This paper presents an experimental study of a three-level hybrid T-type converter using wide-bandgap (WBG) semiconductors, specifically gallium nitride (GaN) and silicon carbide (SiC). The proposed topology takes advantage of the high-voltage capability of SiC MOSFETs and the low switching losses of GaN e-HEMTs, offering significant advantages in efficiency and power density. Futhermore, an adaptive level-shift algorithm is introduced, enabling transitions between two-level and three-level operation modes to enhance fault tolerance and maximise current capability. The behaviour of the power converter and the adaptative algorithm is analysed through experiments performed using a real hybrid T-type converter. Experimental results validate the reliability of the proposed level-shift algorithm. Additionally, the experiments demonstrate that high switching frequencies not only have minimal negative effects on power losses and EMI but also improve THD, confirming the hybrid T-type converter as a robust solution for high-efficiency applications.

This paper presents the optimization of a hybrid microgrid designed to supply energy to a mining plant with a demand of 1.5 MW, connected to a weak power grid. The microgrid integrates several energy sources: solar, wind, microturbine, diesel generator, and battery storage. Energy management is optimized for a 24-hour horizon using the Multi-Objective Gray Wolf Optimizer (MO-GWO). This algorithm allows finding optimal solutions by balancing the minimization of three objectives: energy generation costs, CO2emissions, and flattening of the demand curve from the grid perspective. The development and analysis of the results were carried out in Python, using the Spyder integrated development environment.

The increasing energy consumption of heating, ventilation and air conditioning (HVAC) systems is one of the main concerns in the building sector. Fault detection technologies are now indispensable for energy efficiency and performance improvement. In this paper, a methodology for the robust and reliable fault detection and diagnosis is presented as a two-stage framework composed by an offline stage where the models are built and an online stage that is constantly receiving new samples. The system includes a novelty detection scheme developed using one-class support vector machines (OC-SVM) and a classifier built using SVM. The proposed strategy is applied to a dataset for a single-zone constant air volume air handling unit. The experimental results show that the novelty detection stage adds robustness layer to the typical classification scheme.

In this paper, the optimization of the energy equipment of a factory to be used for meeting the internal demand and to exploit them against the external energy market, adopting a prosumer behaviour that actively bids energy with the utility grid, is studied. The energy infrastructure of the industrial plant is modelled and the sizing optimization problem is mathematically defined and solved using Genetic Algorithms. Four scenarios are considered regarding energy management strategies: Do-nothing, self-consumption, prosumer with non-optimal installation and prosumer with the optimal installation. Results show that the prosumer with optimal installation outperforms other scenarios achieving total energy savings of 47% and a payback period of 8 years, enhancing the participation of industry in the upcoming energy market where distributed energy sources and flexible active clients will have a significant role towards decarbonisation.

This paper deals with the realization of a five-phase cross saturating permanent magnet assisted synchronous reluctance motor (PMaSynRM) drive with vector control based on maximum torque per ampere (MTPA) and flux weakening (FW) control strategies derived from the identified flux maps originating from the finite element analysis (FEM). Tracking of the reference currents in dq1 and dq3 axes is guaranteed with the proposed approach and the voltage and current constraints are not exceeded at any working condition.

Universities have a relevant and essential key role to ensure knowledge and development of competencies in the current fourth industrial revolution called Industry 4.0. The Industry 4.0 promotes a set of digital technologies to allow the convergence between the information technology and the operation technology towards smarter factories. Under such new framework, multiple initiatives are being carried out worldwide as response of such evolution, particularly, from the engineering education point of view. In this regard, this paper introduces the initiative that is being carried out at the Technical University of Catalonia, Spain, called Industry 4.0 Technologies Laboratory, I4Tech Lab. The I4Tech laboratory represents a technological environment for the academic, research and industrial promotion of related technologies. First, in this work, some of the main aspects considered in the definition of the so called engineering education 4.0 are discussed. Next, the proposed laboratory architecture, objectives as well as considered technologies are explained. Finally, the basis of the proposed academic method supported by an active learning approach is presented.

The worldwide energy market is undergoing a transition that will lead to a greener and non-fossil fuel dependent situation in which demand side management and prosumers will play a key role. The digitalization of energetic industrial facilities to create a virtual power plant by forecasting future energy situation and modelling internal energy flow is performed for a specified case study. In this paper the proposal of creating a virtual power plant from an industrial plant is done to benefit from the opportunities raised by the energetic transition. A study of the market and exploitation approach is done. The feasibility of developing a virtual power plant considering future energy situation and internal energy assets is verified by optimizing its final cost in terms of performance against external markets. The results show that there are economic benefits for the owner of the facility while assuring the energy demand and the proper operation of the equipment.

This paper presents the design and structure of a versatile workbench simulator for evaluating the performance of a five-phase inverter and Permanent Magnet assisted Synchronous Reluctance Motor (PMa-SynRM). The simulator allows for adding variations to the modulation techniques, changing the inverter structure's semiconductor device, and calculating the inverter's power losses. It can also facilitate observing the current, voltage, and the joint temperature of the semiconductors devices. Furthermore, we can obtain a perform that is close to an actual PMa-SynRM, depending on the desired conditions of speed and torque. The workbench simulator was developed by combining three software: Matlab/Simulink, PLECS and Altair Flux.

This paper presents a comprehensive analysis of SVPWM for a five-phase VSI based on SiC devices applied to motor drives. The modulation techniques analyzed use medium and large vectors to reach the reference vector. The 2L SVPWM uses two large space vectors, and the generated output signal contain low frequency harmonics. 2L+2M SVPWM uses two large and two medium space vectors. This technique provides good power loss distribution. 4L SVPWM works with the activation of four large space vectors. This modulation is able to generate low common-mode voltage. The performance and main features are analyzed using Matlab/Simulink and PLECS blockset software. Power losses, total harmonic distortion and common-mode voltage are compared and evaluated.

Multiphase Synchronous Machines vary in rotor construction and winding distribution leading to non-sinusoidal inductances along the rotor periphery. Moreover, saturation and cross-saturation effects make the precise modeling a complex task. This paper proposes a general model of multi-phase magnet-excited synchronous machines considering multi-dimensional space modeling and revealing cross-harmonic saturation. The models can predict multiphase motor behavior in any transient state, including startup. They are based on flux maps obtained from static 2D Finite-Element (FE) analysis. FE validations have been performed to confirm authenticity of the dynamic models of multiphase PMaSynRMs. Very close to FE precision is guaranteed while computation time is incomparably lower.

In this work, an optimal power sharing controller for a three-phase Inverter-based Generator (IG) in a synchronous d-q reference frame is presented. The optimization of this controller is computed using a Linear-Quadratic (LQ) tracking index that measures the tracking error. This approach has many advantages regarding to stability and robustness over classical Proportional-Integral (PI) or Proportional-Resonant (PR) controllers that use droop functions for power sharing. In addition, a comprehensive model that represents a grid-connected IG sharing power to the main grid is developed using the superposition principle. This model integrates the Voltage-Current (V-I) and power sharing dynamics in a single state space expression. To the best of our knowledge, although there have been approaches in V-I and power sharing control that improve microgrid stability and transient response, there are no formal methods that integrate both controllers as a single entity. The results of this method were compared against a known Proportional-Resonant controller that use droop functions for power sharing. Results show that the optimal power sharing controller improves transient response, improves power decoupling, and also reduces the quadratic cost associated with microgrid states and inputs.

This article presents a novel methodology for the determination of soiling appearance on photovoltaic panels by means of data analysis of their energy production and operating conditions. The proposed methodology is based on the generation of a daily-based grey box model for each supervised panel, fitted through the sequential quadratic programming optimization approach, and the evolution analysis of the fitted coefficients to determine the appearance of soiling through the calculation of its monotonic drift and slope. The presented approach has been developed in the framework of a CORFO R&D project and validated under real operating conditions in a utility-scale photovoltaic power plant of one axis mount, located in Chile.

This study is focused on the current challenges dealing with electromechanical system monitoring applied in industrial frameworks, that is, the presence of unknown events and the limitation to the nominal healthy condition as starting knowledge. Thus, an industrial machinery condition monitoring methodology based on novelty detection and classification is proposed in this study. The methodology is divided in three main stages. First, a dedicated feature calculation and reduction over each available physical magnitude. Second, an ensemble structure of novelty detection models based on one-class support vector machines to identify not previously considered events. Third, a diagnosis model supported by a feature fusion scheme in order to reach high fault classification capabilities. The effectiveness of the fault detection and identification methodology has been compared with classical single model approach, and verified by experimental results obtained from an electromechanical machine.

A great deal of investigations are being carried out towards the effective implementation of the 4.0 Industry new paradigm. Indeed, most of the machinery involved in industrial processes are intended to be digitalized aiming to obtain enhanced information to be used for an optimized operation of the whole manufacturing process. In this regard, condition monitoring strategies are being also reconsidered to include improved performances and functionalities. Thus, the contribution of this research work lies in the proposal of an incremental learning framework approach applied to the condition monitoring of electromechanical systems. The proposed strategy is divided in three main steps, first, different available physical magnitudes are characterized through the calculation of a set of statistical-time based features. Second, a modelling of the considered conditions is performed by means of self-organizing maps in order to preserve the topology of the data; and finally, a novelty detection is carried out by a comparison among the quantization error value achieved in the data modelling for each of the considered conditions. The effectiveness of the proposed novelty fault identification condition monitoring methodology is proved by means of the evaluation of a complete experimental database acquired during the continuous working conditions of an electromechanical system.

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.

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.

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, and the results are reflected in this paper.

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.

This paper presents a dynamic reluctance network (RN) magnetic equivalent circuit (MEC) analysis of a 55/52-pole five phase outer rotor brushless direct current (BLDC) motor. Winding distribution, stator slotting and iron saturation is included in the model. With this analysis the dynamic characteristics of the tested machine can be calculated. Back EMF and cogging torque responses are compared with the ones obtained from the finite element model (FEM) under no load and load conditions. The computation time of the RN network is significantly low with respect to the FEM analysis. The experimental setup results turn to be in very close matching to those provided by the elaborated models.

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.

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.

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.

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.

A wide study regarding the suitability of data-driven modelling applied to the prediction of thermal convection responses on substation connectors is presented in this paper. The study starts with the compilation of a database with thermal profiles obtained from a finite element method simulation (FEM). Afterwards, we applied partitioning methods in order to increase the number of data sets used for modelling and later evaluate the stability of the learning algorithms. After the modeling process, the accuracy of the model per each data set is measured and the statistics about the errors are analyzed. Normality test are applied to measure the error variance. They bring us information about the error distribution and the stability of the learning algorithms. The study finish when it probes that any data-driven model is computationally less time expensive than any FEM simulation running on this study. Experimental work also confirms that the accuracy of the data-driven models: cascade feed forward neural network and feed forward neural network, can replace the FEM simulations; providing a high accuracy and a low error variance while speeding up the simulation time.

Currently, the Cartesian Genetic Programming approaches applied to regression problems tackle the evolutive strategy from a static point of view. They are confident on the evolving capacity of the genetic algorithm, with less attention being paid over alternative methods to enhance the generalization error of the trained models or the convergence time of the algorithm. On this article, we propose a novel efficient strategy to train models using Cartesian Genetic Programming at a faster rate than its basic implementation. This proposal achieves greater generalization and enhances the error convergence. Finally, the complete methodology is tested using the Australian electricity market as a case study.

This document analyses the large-signal stability for an inverter-based generator such as photovoltaic and wind power sources. The objective of this study is to determine the stability region taking into account the electrical and control signal of the generator. The generator uses the concept of the electrostatic machine for the model of the generator. Finally, the applied procedure to find the Lyapunov's function is the Popov method, which not only permits to generate a valid function but also to determine the stability region of the system.

Model predictive control algorithms have recently gained more importance in the field of power electronics and motor drives. One of the important categories of model predictive control methods is improved deadbeat control in which the reverse system model is used to calculate the appropriate inputs for the next iteration of controlling process. In this paper, a new improved deadbeat algorithm is proposed to control the stator currents of a five-phase BLDC machine. Extended Kaiman filter is used in the structure of proposed controlling method, and system model equations are used to calculate the appropriate voltages for the next modulation period. Proposed controlling method is evaluated by simulations in MATLAB environment.

This paper presents an energy optimization methodology applied on industrial plants with multiple energy carriers. The methodology combines an adaptive neuro-fuzzy inference system to calculate the short-term load forecasting of a plant, and the sequential quadratic programming algorithm to optimize its energy flow. Furthermore, the mathematical models of the plant's equipment are considered into the optimization process, in order to calculate the dynamic system response and the equipment's inertias. The final algorithm optimizes the operation of the plant in order to satisfy the energy demand, minimizing several optimization criteria. The methodology has been tested and evaluated in an automotive factory plant using real production and consumption data.

A model of power demand represents the foundation of any intelligent Energy Management System, and its accuracy is the key factor determining the performance of such system. In order to improve the accuracy of the modeling process, a multi-model approach based on a Hierarchical Clustering of similar load behaviors is presented. The clustering algorithm joins similar data subsets in groups that are modelled separately using Adaptive Neuro-Fuzzy Inference Systems. Thus, each of the obtained models addresses only the characterization of one behavior, which provides better accuracy than classical approaches based on a single model, in addition to being easier and faster to train. During the training process of the models, an input selection technique based on Genetic Algorithms is proposed to search and select the best combination of inputs. The use of search algorithms allows to reduce the complexity of this task while maintaining the system performance, which represents a significant time saving of expert staff. The proposed approach is validated by means of experimental data from an automotive manufacturing plant. In addition to improving the forecasting accuracy, this methodology automates the segmentation of the load profiles into models and the selection of their inputs, as well as improving parallelization to effectively reduce the computation time.

This paper deals with fault tolerant model predictive control (MPC) of five-phase brushless direct current (BLDC) motors. The model of machine is used to estimate the stator currents for the next modulation period, and then, the required voltages are calculated to minimize the current errors. Center aligned five-phase space vector modulation (SVM) is used in the inverter section. The proposed controlling strategy is evaluated via simulations in MATLAB environment.

In this paper the effect of the magnets shape on the AFPMM performance under a demagnetization fault has been analyzed by means of 3D-FEM simulations. Demagnetization faults in permanent magnet synchronous motors (PMSMs) may generate specific fault harmonic frequencies in the stator currents, output torque and the zero-sequence voltage component (ZSVC) spectra the ones can affect motor behavior, and so these parameters have been studied and compared, for each magnet configuration in each condition. These analyses are carried out to find out the more suitable geometry for an operation under healthy and faulty condition.

It is a fact that the short-term load forecasting (STLF) in the user side is growing interest. Consequently, intelligent energy management systems (iEMSs) are including this capability in order to take autonomous decisions. In this context, this paper presents a new STLF scheme based on Adaptative Networks Fuzzy Inference Systems (ANFIS). This ANFIS has an exponential output membership functions (e-ANFIS) and has been trained by means of a novel evolutionary training algorithm (ETA). Due to the computational burden required by ETA, parallel computing was used to eliminate this problem especially for embedded applications. This new scheme has been tested with real data from an automotive factory and it shows better results in comparison with typical adaptative network structures (neural network and ANFIS).

This study deals with the fault tolerant vector control strategies of a five-phase permanent-magnet (PM) machine. The analysis is focused on the effect of accessible neutral point under faulty conditions. Open circuit fault of one and two phases are considered, and proper control strategies are proposed to reduce the amplitude of currents in the remaining healthy phases. Simulations under both healthy and faulty conditions have been undertaken, and the effect of accessible neutral point on current amplitudes and torque ripple is evaluated.

At present, the intelligent energy management systems (IEMS) are used to maximiz the relation between productivity and cost using a variety of energy sources. In this work, we present a method of short-time load forecasting, using the ANFIS model and a component of preprocessing based in the discrete wavelet transform; the models was implemented in the user-side, analyzing real data of a factory in order to test the proposed algorithm.

This paper presents a study and analysis of the distorting effects of clamped diodes in multilevel DCI-NPC topology applied to high power and high quality audio-amplifier. The main distorting sources of error are characterized, which are due to the clamped-diodes non-idealities; and they are evaluated for typical commercial diodes values and working conditions. Through this study the distorting contribution of each non-ideality can be quantified, and the influence of the system operating parameters, such as reactive current angle and modulation index, are highlighted. Finally, it is presented the table of optimal parameters that a commercial diode should have to ensure proper operation of the amplifier under rated power and quality specification.

In this paper an evolutive algorithm is used to train an adaptative-network-based fuzzy inference system (ANFIS), particularly a genetic algorithm (GA). The GA is able to train the antecedent and consequent parameters of an ANFIS, which is used for energy load profile forecasting in an automated factory. This load forecasting is useful to support an intelligent energy management system (IEMS), which enables the user to optimize the energy consumptions by means of getting the optimal work points, scheduling the production according to these points, etc. The proposed training algorithm showed excellent results with complex plants like industrial energy consumers in the user-side, where the randomness of the loads is higher than in utility loads. Real data from an automated car factory were used to test the presented algorithms. Appropriated results were obtained.