Amel Adouni

Three-phase induction motors are considered to be the workhorse of industry. Therefore, induction motor faults are not only the cause of users’ frustrations but they also drive up the costs related to unexpected breakdowns, repair actions, and safety issues. One of the most critical faults in three-phase induction motors is related to the occurrence of inter-turn short circuits, due to its devastating consequences. The topic of inter-turn short-circuit faults in three-phase induction motors has been discussed over recent decades by several researchers. These studies have mainly dealt with early fault detection to avoid dramatic consequences. However, they fall short of addressing the potential burnout of the induction motor before the detection step. Furthermore, the cumulative action played by an inevitable degree of unbalanced supply voltages may exacerbate such consequences. For that reason, in deep detail, this paper delves into the thermal analysis of the induction motor when operating under these two harsh conditions: unbalanced supply voltages and the presence of the most incipient type of inter-turn short-circuit condition—a short-circuit between two turns only. In this work, the finite element method has been applied to create the faulty scenarios, and a commercial software (Flux2D) has been used in order to simulate the electromagnetic and thermal behavior of the machine for various degrees of severity of the aforementioned faulty modes. The obtained results confirm that the diagnostic tools reported in the literature might not be effective, failing to warrant the required lead time so that suitable actions can be taken to prevent permanent damage to the machine.

As one of the most important assets of the industry, it is crucial to fully characterise all failure modes showing potential to degrade the normal operation of induction motors (IMs). One of the failure modes which lacks detailed knowledge and proper diagnostic tools is the inter‐turn short‐circuit (SC) fault. Given the severity of such failure mode, it is pivotal to ensure that incipient fault symptoms are correctly identified, thus preventing critical damages to the IM. Unfortunately, the state‐of‐the‐art does not provide enough data to confirm whether the available diagnostic tools act out in due time to avoid permanent damage to the faulty IM. To evaluate the impacts of this failure mode in the temperature of the stator windings of an IM, this paper presents the results obtained from two alternative thermal models of the same IM, resorting to the lumped parameter thermal network method and to the finite elements method. The results confirm that diagnostic tools reported in the literature might not be effective, failing to correctly diagnose an inter‐turn SC fault and to warrant the lead time required to take actions suitable to prevent permanent damage to the IM.

Under abnormal electrical supply conditions, induction motors can easily reach an overheating condition. A continuous operation under thermal stress significantly decrements the motors useful lifetime. In this paper a short-time thermal transient analysis, based on a Lumped Parameter Thermal Network (LPTN), is performed in order to evaluate the thermal behaviour of an asynchronous machine operating under supply voltage unbalance. Three types of supply unbalance are considered in this paper: unbalanced voltage amplitude, unbalanced phase, and a combination of both. The analytical model is used to estimate the temperature in different points of the stator. This model presents a reasonable compromise between simplicity, accuracy, and rapidity. The obtained results are validated using an experimental prototype.

Synchronous Reluctance Machines (SynRMs) are facing a revamped industrial interest. A comparison between SynRMs and other types of electric machines allow listing the advantages and proving the choice of such machines. This paper presents a review of SynRMs, regarding particularly their thermal analysis. First of all, a description of the basic principles of SynRM is illustrated. The thermal analysis is based mainly on the study of the heat sources and the heat extraction mechanisms which are listed and described. Nevertheless, the coupling effects between the thermal field and the magnetic material parameters are discussed. Then, the three heat extraction phenomena (conduction, convection and radiation) are analyzed. Along the work, important facts about the thermal analysis are discussed. A comparison between the different approaches used for the thermal analysis is highlighted, and new developments and perspectives are identified.

This paper presents a control strategy for a grid interface system fed by a photovoltaic (PV) source. A two stages topology with boost converter followed by a three phase VSI (voltage source Inverter) is used for the grid interface. The boost DC converter is controlled using an Incremental conductance maximum power point tracking technique in order to feed the maximum of power to the grid and achieve fast control response to transients and changes in weather conditions. The output of the boost converter is adaptive to the variation of the grid voltages to reduce the power losses and feeding the grid with an extra power. The proposed grid connected PV system is tested considering grid voltages variations and the obtained results confirm the highest dynamic performances mainly when a sudden decrease in the grid voltages.

The photovoltaic (PV) system productivity is a decisive factor. To achieve a high productivity, the system availability should be checked. So even under a faulty situation, the system should operate continuously. As a proposed solution, the fault tolerant control (FTC) is recommended. In advance of FTC strategy, the fault diagnosis should be achieved. This paper deals with the open circuit fault occurred in the DC-DC converter. The Artificial Neural Network (ANN) is used to estimate the current and the voltage feeding the resistive load. These signals are compared to the measured ones. So, the proposed approach allows generating different signals in order to detect and to isolate the open circuit fault. The signals are the residuals and the flags. The fault is successfully detected and isolated.

This paper proposes a data driven approach for dip voltage fault detection and identification using the grid current vector trajectory in the stationary reference frame. Three features are extracted for the different operating conditions to build the database and analysed using Linear Discriminant Analysis to identify the fault type and subtype. In the subspaces spanned by the factorial components the four faults and eight out of nine faults subtype are successfully identified and isolated with an error rate less than 5%. Simulation results prove the efficiency of the proposed algorithm.

Any diagnosis procedure should be rapid and efficiency. In the presence of the noise and unknown inputs, the diagnosis procedure could generate false alarms. This paper focuses in the fault detection, isolation and identification when a fault affected the sensors of a DC motor worked in disturbance environment. The chosen technique should take account the two issues: the noise and the unknown inputs. As a solution the Artificial Neural Network is adopted in order to generate a robust residuals and therefore to minimize the false alarms. A new ANN architecture is proposed to achieve this purpose. The simulation results proved the rapidity and the efficiency of the proposed procedure.

In this paper, fault detection and an isolation technique for an insulated-gate bipolar transistor open-circuit fault in a voltage source inverter are presented. This technique consists of analysing the pole voltage and providing the detection and the location of simple, simultaneous and multiple faults. Open-circuit faults can be detected by sensing the pole voltage of each leg and comparing it with the theoretical one. To improve the calculation speed and reliability of this technique and to avoid false diagnosis alarms, the fault detection and isolation scheme is based on a novel model of pole voltage taking into account the time delays due to the turn-on and turn-off process of the power switches. This method reduces the detection time and is applied for open-loop or closed-loop faults in a transient or steady state.