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Even though planning the educational action to optimize student performance is a very complex task, teachers typically face this challenging issue with no external assistance. Previous experience is, in most cases, the main driving force in curriculum design. This procedure commonly overlooks the students' perception and weakly integrates the students' feedback by using a non-systematic approach. Furthermore, transverse competences are, unfortunately, typically omitted in this procedure. This work suggests the use of a predictive tool that determines the optimal application time of different methodological instruments. The suggested method can be used for an infinite number of scenarios of promoted competences. The results can be regarded as a guide to modify the course structure, but, more importantly, it offers valuable information to understand better what is occurring in the teaching-learning process and detect anomalies in the subject and avoid the students' exclusion. The predictive scheme simultaneously considers the teacher's perspective, the student's feedback, and the previous scores in a systematic manner. Therefore, results provide a broader picture of the educational process. The proposal is assessed in a course of Electrical Machines at the University of Malaga during the academic year 2021–2022. [ABSTRACT FROM AUTHOR]

Multiphase (more than three phases) drives possess interesting advantages over conventional three-phase drives. Over the last years, various topics related to the extension of the classical control schemes to these specifics drives have been covered in depth in literature, such as vector control of a six-phase induction machine with two sets of three-phase stator windings spatially shifted by 30 electrical degrees (also called asymmetrical dual three-phase ac machine). In this paper, a model-based predictive control (MBPC) for the current regulation of asymmetrical dual three-phase ac machines is analyzed. MBPC overcomes the difficulties of multiphase current control, avoiding complex controllers and modulation techniques, but at the expense of an increased computational cost. Simulation results are provided to examine the potential of the control method. The influence of the number of voltage vectors considered to evaluate the predictive model is studied, and different cost functions are analyzed. The computation time needed for the implementation of the control method is discussed to prove its real-time feasibility. Finally, experimental results are given to illustrate the capability of the control method. Index Terms--Analysis, asymmetrical dual three-phase induction machine, modeling, multiphase drives, predictive control.

Multiphase (more than three phases) drives exhibit interesting advantages over conventional three-phase drives. Over the last years, topics related to the extension of control schemes to these specific drives have been covered in depth in the literature. Direct torque control and predictive current control are normally used in conventional ac drives when fast electrical dynamic performance is required. In this paper, a one-step modulation predictive current control technique is proposed for asymmetrical dual three-phase ac drives. Based on the use of a predictive model including the motor and the inverter, the control algorithm determines the switching state which minimizes errors between predicted and reference state variables. The period of application of the selected switching state is then obtained, resulting in a submodulation method. The proposed predictive current control algorithm uses a prediction horizon of one sampling period; however, two switching states are applied during the sampling period. The switching states are the selected optimum active vector and a null voltage combination. Simulation and experimental results are provided to examine the features of the control method. Performances, advantages, and limitations are also discussed. Index Terms--Asymmetrical dual three-phase ac machine, multiphase drives, predictive current control.

The prevailing tendency in modern university reforms is towards 'how people learn,' following a learner-centered approach in which the learner is the main actor of the teaching-learning process. As a consequence, one of the key indicators of the teaching-learning process is the measurement of learner satisfaction within the classroom. Learner satisfaction has traditionally been measured using survey responses to a standard learning survey. However, more scientific analysis should be performed to assess adequately not only learner satisfaction but also the main dimensions that have a positive impact on learner satisfaction. The purpose of this paper is to define a structural and measurement model in which causal relationships among these different dimensions are adequately established. The methodology is based on a multivariate regression model (Structural Equation Models) to establish scientifically a structural model for learner satisfaction within a classroom, measuring its validity and reliability. The proposed approach has been applied to model learner satisfaction in an electronic instrumentation course at the University of Seville, Spain. The results and implications of this study will contribute to improve student satisfaction with respect to the dimensions considered. Index Terms--Educational technology, electronic equipment, laboratories, learning systems, planning.

Among distributed energy resources, wind power has recently showed great potential and it is being promoted in many countries. The wind power penetration increase and the trend of the wind farms to enter the market, makes necessary the development of new prediction tools. Prediction tools have been fully verified and used for demand forecast and, more recently, to predict the market prices. Among the different prediction methods proposed, the initial and more deeply verified were the statistical ones. In the present work, time series statistical methods are used to explore and assess the achievable improvement over a persistent model. Both autoregressive (AR) models that consider wind power time series and autoregressive with exogenous variable (ARX) models which include wind speed time series are used to carry out the prediction. An analysis of the appropriate parameters for the models is carried out and shows, for different wind farms, comparative results for persistent, AR, and ARX models with 6, 12, and 24 h forecast which can be useful for the day-ahead and hour-ahead electricity markets. The study also includes a comparative analysis of the different wind farms considered both independently and in an aggregated manner. The forecast improvement due to the use of ARX models and to the wind farm aggregation is fully assessed. DOI: 10.1061/(ASCE)0733-9402(2007)133:3(172) CE Database subject headings: Wind energy; Forecasting; Time series analysis; Regression analysis; Energy resources; Models.

This paper proposes and details a course organization methodology based on learner satisfaction achievement. The approach follows the prevailing tendency in modern university reforms which are primarily concerned about 'how people learn.' As a consequence, the learner has been placed as the main actor of the teaching-learning process. Nevertheless, the current learning literature has not addressed the measurement of learner satisfaction within laboratory and practical subjects. This study develops a general and comprehensive methodology for learner satisfaction measurement in practical subjects. As a case example, the proposed methodology has been applied to an electronic instrumentation and measurement course, confirming the variables with a significant influence on learner satisfaction and becoming the starting point for curriculum redesign based on the learner satisfaction approach. To assess the improvement of the proposed course organization methodology, a comparison with previous academic years has been performed showing the students' rated score evolution. Results fully confirm the validity of the technique and the novel course organization. Index Terms--Course organization, educational technology, electronic engineering, electronic instrumentation and measurement lab, learner satisfaction.

Sensorless drive control has been widely studied in recent years due to the numerous advantages regarding potential failures of position sensors, especially in applications such as automotive or aerospace. Among vector-control drives, indirect rotor-flux-oriented control (IRFOC) type is one of the most popular and tested options. However, it is still a challenging field since several aspects can be improved, such as the low-speed behavior, parameter detuning, and current control. The present scheme includes temperature estimation to correct the deviation in steady state, a new control scheme with skin-effect estimation to improve the transient accuracy, and overcurrent protection to be able to control the stator currents while allowing a good performance. The parameter estimation is carried out using lumped-parameter models, the control scheme is modified and is able to account for static friction, and the overcurrent protection improves the performance allowing transient currents over the rated value. The validity and usefulness of the proposed scheme is experimentally tested on a TMS320C31 digital signal processor (DSP) from the Simulink/Matlab environment. Index Terms--Deep-bar effect, parameter estimation, thermal effect, vector control.