Availability and continuous operation under critical conditions are very important in electric machine drive systems. Such systems may suffer from several types of failures that affect the electric machine or the associated voltage source inverter. Therefore, fault diagnosis and fault tolerance are highly required. This paper presents a new robust deep learning-based approach to diagnose multiple open-circuit faults in three-phase, two-level voltage source inverters for induction-motor drive applications. The proposed approach uses fault-diagnosis variables obtained from the sigmoid transformation of the motor stator currents. The open-circuit fault-diagnosis variables are then introduced to a bidirectional long short-term memory algorithm to detect the faulty switch(es). Several simulation and experimental results are presented to show the proposed fault-diagnosis algorithm’s effectiveness and robustness.

This paper focuses on a water pumping system fed by a hybrid (PV–Wind) generator. The water pumping system uses centrifugal pumps driven by variable speed Induction Motors (IM) controlled by a Field Oriented Control (FOC). The absence of battery storage to decouple sources and power demand is the main originality of the contribution, together with the typical adaptation of the FOC strategy. Furthermore, the absence of battery storage will consequently lead to fixing the system operating point at a steady state which is imposed both by the intermittent renewable energy sources and by the hydraulic load characteristics. The basic idea is then to adapt the system impedance by using the two degrees of freedom offered by the power source inverter in order to control, firstly, the DC bus voltage and, secondly, the rotor flux of the induction machine; the adaptation of the FOC strategy is based on this idea. Simulation results clearly confirmed by experimental investigations show the satisfying performance of the system even with variable powers of the intermittent renewable source.

This paper presents the experimental results of an original control strategy for motor-pump implemented on a dSPACE controller board. The Power Field Oriented Control strategy developed in this paper is used to drive a motor-pump fed by intermittent renewable sources. This system is dedicated to autonomous applications for pumping or water desalination for remote areas without access to hydraulic and electric networks. This control strategy allows controlling the DC bus voltage without using batteries in the system. The DC voltage control is achieved through the control of the inverter used for the motor-pump. Experimental results for variable power source are presented and confirm the effectiveness of the proposed control strategy.

The interdependence of producing water and energy cleanly is the most inspiring concern of the recent research study in the world. In this context, this paper aimed to investigate the water-energy nexus with an original viewpoint in order to consolidate the interdependence between water and energy and unify their cooperative relationship. Hence, exploiting the energy sustainability of a grid-connected photovoltaic generator (GCPV) in providing water is authentic, moreover, capitalizing the water station, which is composed of three identical pumping units, to satisfy the grid code (GC) of a GCPV is original. To do that, sophisticated control strategies are used. On the side of GCPV, Voltage Oriented Control (VOC) strategy is assumed to regulate the DC bus voltage and to control the injected reactive power into the grid. On the side of the water station, the dynamic of each pumping unit is controlled using Power-Field Oriented Control (P-FOC) strategy. By manifesting a power control loop, this strategy is the leading technic to reach the satisfaction of the GC and the enhancement of the Low-Voltage Ride Through (LVRT). The second purpose of this paper consists of establishing original energy management by taking into account the different constraints imposed by the system, which are the intermittence and the fluctuations of the solar array, the Tunisian GC, and the tank capacity. Performance analysis of the proposed control strategies and the developed energy management are carried out in small-scale power range which is convenient to be implemented in agriculture applications in rural areas.

Water desalination systems integrated with stand-alone hybrid energy sources offer a remarkable solution to the water–energy challenge. Given the complexity of these systems, selecting an appropriate energy management system is crucial. In this regard, employing artificial intelligence techniques to develop and validate an energy management system can be an effective approach for handling such intricate systems. Therefore, this paper presents an ANN-based energy management system (ANNEMS) for a pumping and desalination system connected to an isolated hybrid renewable energy source. Thus, a parametric sensitivity algorithm was developed to identify the optimal neural network architecture. The water–energy management-based supervised multi-layer perceptron neural network demonstrated effective power sharing within a short time frame, achieving accuracy criteria of RMSE, R, and R² between the actual and estimated electrical power of the three motor pumps. The ANNEMS is defined to facilitate real-time power sharing distribution among the various system motor pumps on the test bench, considering the generated power profile and water tank levels. The proposed strategy employs power field oriented control to maintain DC bus voltage stability. Experimental results from the implementation of the proposed ANNEMS are provided. Therein, the power levels of the three motor pumps demonstrated consistent adherence to their reference values. In summary, this study highlights the significance of selecting appropriate energy management for real-time experimental validation.

This paper studies an original energy management system with control strategy dedicated to an autonomous pumping system fed by intermittent renewable source without battery storage. Power Field Oriented Power is used to control the DC voltage of the source and the rotor flux of the motor-pump. As the motor pump is power controlled, the system operating point is imposed by the source given the hydraulic load characteristic. The basic idea is to study theoretically and experimentally the system behavior under variable given power and many hydraulic loads. Finally power management strategy is deduced allowing satisfying performances of the system.