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This paper describes a novel Electrical Machine and Power Electronic Training Tool (EM&PE[subscript TT]), a methodology for using it, and associated experimental educational activities. The training tool is implemented by recreating a whole power electronics system, divided into modular blocks. This process is similar to that applied when analyzing a piece of industrial equipment by the functions of its various parts. The EM&PE[subscript TT] is divided into five areas: the power block, the electronic switch command, instrumentation, the control system, and the application environment. The training tool lets a user analyze each functional block and the relationships between functional blocks; it can be used either in local or remote mode, thus ensuring optimal effectiveness of the proposed learning methodology. The students can redesign the hardware and software blocks before reassembling them; this allows various different experimental projects to be run concurrently in the same training tool. The results presented here show that EM&PE[subscript TT] improves engineering students' technical skills and their motivation in experimental work. (Contains 14 figures and 1 table.)

The training tool presented here is integrated into a set of electric machine and power electronic training tools (EM&PETT) at the Basque Country University and consists of a motor-generator group with two twin 30 W permanent magnet d.c. motors, three batteries and an array of three solar panels. Speed and torque control methods for d.c. machines, tracking of maximum power point algorithms for photovoltaic systems and control of energy recovery of the d.c. generator when it works as an active mechanical load are experimental procedures within the scope of the EM&PETT. The fundamental idea behind this training tool is to introduce engineering students to the concepts of electric propulsion control through electronic power converters, with energy efficiency criteria and solar energy generation. Finally, both the operation and adjustment of the system and the display of the variables that are included in the EM&PETT can be done locally or remotely by several users. [ABSTRACT FROM AUTHOR]

A novel sensorless integral sliding-mode control for induction motors is discussed. Besides, the control scheme provides global asymptotic speed tracking in the presence of unknown parameters and load torque. For this purpose, the control scheme makes use of an improved method of speed estimation operating on the principle of a speed adaptive flux and current observer. As it is known, an observer is basically an estimator that uses a plant model and a feedback loop. In particular, an observer is constructed, which provides a speed estimation on the basis of the measured stator voltages and currents. Then a sliding-mode controller with an integral switching surface is considered. The closed-loop stability of the proposed controller is proved under parameter uncertainties and load disturbances, using the Lyapunov stability theory. Simulated results show that the proposed controller, jointly with the proposed observer, provide high-performance dynamic characteristics and that this scheme is robust with respect to plant parameter variations and external load disturbances. Finally, the feasibility of the proposed control scheme is proved by means of experimental results over a real induction motor. [ABSTRACT FROM AUTHOR]

This paper presents the design and implementation of a supervisory control and data acquisition (SCADA) system for automatic fault detection. The proposed system offers advantages in three areas: the prognostic capacity for preventive and predictive maintenance, improvement in the quality of the machined product and a reduction in breakdown times. The complementary technologies, the Industrial Internet of Things (IIoT) and various machine learning (ML) techniques, are employed with SCADA systems to obtain the objectives. The analysis of different data sources and the replacement of specific digital sensors with analog sensors improve the prognostic capacity for the detection of faults with an undetermined origin. Also presented is an anomaly detection algorithm to foresee failures and to recognize their occurrence even when they do not register as alarms or events. The improvement in machine availability after the implementation of the novel system guarantees the accomplishment of the proposed objectives. [ABSTRACT FROM AUTHOR]