Man Zhang

Modélisation multiphysique du couplage électromagnétique/mécanique et développement de contrôles de vibration appliqués aux machines à réluctance variable: En raison de ses avantages inhérents, tels que son faible coût, sa fiabilité élevée, sa capacité de fonctionnement à grande vitesse et en environnements difficiles, la machine à réluctance variable (MRV) est une solution attrayante pour l'industrie automobile. Cependant, la traction automobile est une application pour laquelle le comportement acoustique du groupe motopropulseur doit être particulièrement considéré, dans l'optique de ne pas dégrader le confort des passagers. Afin de rendre la MRV compétitive pour cette application automobile, le travail présenté se concentre sur plusieurs méthodes de contrôle cherchant à améliorer le comportement acoustique des MRV en réduisant les vibrations d'origine électromagnétique. Un modèle multi-physique électromagnétique / mécanique semi-analytique est proposé à partir de résultats de simulation numérique obtenus par la méthode des éléments finis. Ce modèle multiphysique est composé de modèles électromagnétiques et structurels, qui sont reliés par la composante radiale de la force électromagnétique. Deux méthodes de contrôle sont ensuite proposées. La première réduit la vibration en faisant varier l'angle de coupure du courant, la fréquence du la variation étant basée sur les propriétés mécaniques de la structure MRV. De plus, une fonction aléatoire uniformément distribuée est introduite pour éviter une composante fréquentielle locale de forte vibration. Une seconde méthode est basée sur le contrôle direct de la force (DFC), qui vise à obtenir une force radiale globale plus douce pour réduire les vibrations. Un adaptateur de courant de référence (RCA) est proposé pour limiter l'ondulation de couple introduite par le DFC, provoquée par l'absence de limitation de courant. Cette seconde méthode de réduction des vibrations appelée DFC & RCA est évaluée par des tests expérimentaux utilisant un prototype de MRV 8/6 afin de montrer sa pertinence. Une solution de partitionnement hardware/software est proposée pour implémenter cette méthode sur une carte FPGA utilisée en combinaison avec un microprocesseur.

Dry-type air-core shunt reactors are integral components in power transmission and distribution networks, designed to control reactive power and enhance system stability. However, inter-turn short-circuit faults (ISCFs) are common occurrences in shunt reactors, which are caused by various factors, including manufacturing defects, insulation degradation, or operational stresses. At the early stage of the ISCFs, the current does not reach a sufficient level to activate the protective equipment. These faults may lead to serious consequences, such as overheating, insulation breakdown, and even catastrophic failures, posing risks to the entire power system. Therefore, developing an effective and reliable detection method for ISCFs at the early stage is paramount. In this paper, a new method named the fault detection factor (FDF) based on equivalent resistance is presented to detect the slight ISCFs in dry-type air-core shunt reactors considering insulation resistance. In addition, the effect of noise signal existence in the monitoring process is taken into account. A moving average filter is adopted to guarantee both the sensitivity and the reliability of the proposed method. Ultimately, the simulation results of the FDF under different conditions are presented, which show the effectiveness and potential of the proposed method in observing and monitoring slight ISCFs.

A reactor is an important piece of equipment used for reactive power compensation in power system and has a significant impact on the safe operation of power system. Thermal behavior is one of the main causes of reactor failures. For an accurate analysis of the thermal behavior of reactors, electromagnetic–thermal–fluid multi-physics coupling modeling is chosen. However, there is a huge difference in size between the overall structure of the reactor and its insulating material, which makes it difficult to perform mesh generation, resulting in dense mesh and significantly increased solution degrees of freedom, thus making the solution of the reactor’s multi-physics field model very time-consuming. To address this, this paper proposes a simplified processing method to accelerate the solution calculation of the reactor’s multi-physics model. This method calculates the equivalent turns of each encapsulate with parallel coils in the reactor, simplifying the encapsulate into a single-layer coil, thereby greatly reducing the division and solution degrees of freedom of the multi-physics model, and thus accelerating the simulation calculation. Taking a BKDCKL-20000/35 dry-type air-core shunt reactor as an example, the outer diameter of the coil is nearly 12,000 times bigger than the coil insulation, which is a huge size difference. Both refined models and simplified models are established. Compared to the simulation results of the detailed model, the simplified model demonstrates good accuracy; the maximum relative error of temperature is just 2.19%. Meanwhile, the computational time of the simplified model is reduced by 35.7%, which shows promising effectiveness and significant potential for applying the optimization design and operation prediction of dry-type air-core shunt reactors for enhanced thermal performance.

With the steady development of technology, electric motors (EMs) have become one of the most important components in modern industry. To ensure stable industrial production, detecting and classifying the EM faults is crucial. A novel intelligent deep-learning-based multi-fault detection method for EMs under varying working conditions is proposed in this paper. This method involves two steps: first, a 2D convolution network without pooling layer is proposed to extract features from raw EM data. In addition, a long short-term memory (LSTM) network is applied to extract the fault features for comparison. Second, a capsule network (Caps-Net) based on a dynamic routing algorithm is used as a classifier to realize intelligent multi-fault detection and improve the generalization performance of the proposed model. The proposed method is applicable to raw physical signals of EMs, which improves the overall efficiency of the fault detection. Moreover, the proposed method has a strong generalization ability. The simulation results demonstrate that the proposed approach can achieve higher accuracy than various benchmark methods. Moreover, its accuracy is at least 3% and 10% higher than those of other state-of-the-art models under two working conditions, in which the load type and size of the EM are changed, respectively.

This paper presents a hardware/software implementation for the vibration reduction control of a switched reluctance machine (SRM). A mixed partitioning-based architecture is proposed to make full use of both microprocessor and Field-Programmable Gate Array (FPGA) resources. The aim of the proposed controller is to control directly the radial forces in the stator teeth allowing a significant vibration reduction compared to regular controller applied to SRM. However, if the control is software-only implemented, it suffers from performance degradation due to the high execution time required by this implementation solution. For this reason, the control system implementation is partitioned. The speed control is implemented using a microprocessor, while the current control and direct force control are implemented in FPGA. This solution provides high dynamic response especially needed to control the radial force and the currents. The effectiveness of the hybrid implementation is verified by experimental results.

This paper presents an improved finite-reluctance approach (IFRA) based on the simple synergy between reluctance network and complementary distributed magnetomotive forces (MMFs). In the IFRA, special attention is paid on the distributions of MMFs in the stator slots. Additional vertical MMFs are introduced to the local flux loop. The effects of the MMFs distribution are investigated. The comparison results among IFRA, finite-reluctance approach (FRA), and finite-element method (FEM) validate its accuracy both under linear and nonlinear conditions. Better results are obtained compared to the existing FRA, verifying its potential.

In this paper, a new vibration reduction control of the switched reluctance machine (SRM) is proposed that takes into consideration the undesirable effects of the torque ripple. The control approach aims to reduce the variation of the sum of radial forces while optimizing the control parameters of the machine to achieve the torque ripple minimization. Thus, a reference current adapter is used in order to handle the trade-off between the vibration reduction and the torque ripple reduction. It produces an auto-tuning reference current that limits the variation of the torque and the total radial force. The effectiveness of the proposed control strategy is proved by simulation and experimental results. Comparisons with conventional control methods investigating vibrations, noise and torque ripple are provided, which show the advantages of the control approach based on an auto-tuning reference current.