Design and optimization of synchronous reluctance machine for medium-speed flywheel energy storage system (FESS) applications is presented in this paper. High efficiency and high torque density are the main design criteria of the motor/generator system for electromechanical storage systems. Moreover, high salience ratio and appropriate stator design are required to achieve a high torque density and acceptable efficiency in synchronous reluctance machine. In this paper, the effects of key design parameters on the performance of the motor/generator system are investigated and an optimization procedure based on response surface methodology coupled to finite-element model is performed to achieve optimum value for stator and rotor parameters. The finite-element simulation results confirm that the performance of FESS improves significantly in comparison with initial design, by using the proposed design procedure. Finally, a prototype motor/generator system is implemented and experimental results are presented.

This paper presents a robust model-based technique to detect multiple faults in permanent magnet synchronous motors (PMSMs), namely inter-turn short circuit (ITSC) and encoder faults. The proposed model is based on a structural analysis, which uses the dynamic mathematical model of a PMSM in an $abc$ frame to evaluate the system’s structural model in matrix form. The just-determined and over-determined parts of the system are separated by a Dulmage–Mendelsohn decomposition tool. Subsequently, the analytical redundant relations obtained using the over-determined part of the system are used to form smaller redundant testable sub-models based on the number of defined fault terms. Furthermore, four structured residuals are designed based on the acquired redundant sub-models to detect measurement faults in the encoder and ITSC faults, which are applied in different levels of each phase winding. The effectiveness of the proposed detection method is validated by an in-house test setup of an inverter-fed PMSM, where ITSC and encoder faults are applied to the system in different time intervals using controllable relays. Finally, a statistical detector, namely a generalized likelihood ratio test algorithm, is implemented in the decision-making diagnostic system resulting in the ability to detect ITSC faults as small as one single short-circuited turn out of 102, i.e., when less than 1% of the PMSM phase winding is short-circuited.

This paper presents a detailed analysis of stator winding inter-turn Short Circuit (ITSC) faults, taking the cross effects in the three phases of a permanent magnet synchronous motor (PMSM) into account by considering insulation degradation resistances. A PMSM with series coils in each phase winding is selected as a case study.The ITSC in one coil of each phase winding is modelled based on deformed fluxes or inductance variations caused by flux linkages, depending on the distribution of the coils in the same phase winding. Different fault ratios are investigated to evaluate different fault severity and scenarios. Therefore, three-phase faulty coils within three-phase winding analysis dynamics will constitute sixth-order assessments. The proposed faulty PMSM model is verified by a 2-D finite element analysis (FEA), showing show a good agreement between the proposed model and FEA.

This paper presents a fault diagnosis method based on structural analysis of permanent magnet synchronous motors (PMSMs), focusing on detecting and discriminating two of the most common faults in PMSMs, namely demagnetization and inter-turn short circuit faults. The structural analysis technique uses the dynamic mathematical model of the PMSM in matrix form to evaluate the system’s structural model. After obtaining the analytical redundancy using the over-determined part of the system, it is divided into redundant testable sub-models. Four structured residuals are designed to detect and isolate the investigated faults, which are applied to the system in different time intervals. Finally, the proposed diagnostic approach is numerically verified through a simulation of an inverter-fed PMSM and white Gaussian noise are added to the measured signals from the motor to verify its diagnosis performances.

This paper presents a model-based fault diagnosis method to detect sensor faults in permanent magnet synchronous motor (PMSM) drives based on structural analysis technique. The structural model is built based on the dynamic model of the PMSM in matrix form, including unknown variables, known variables, and faults. The Dulmage-Mendelsohn (DM) decomposition is applied to evaluate the redundancy of the model and obtain redundant testable sub-models. These testable redundant sub-models are used to form residuals to observe the system state, and distinguish between healthy and faulty conditions. This work investigates faults in eleven sensors in a PMSM drive, thus nine structured residuals are designed to detect and isolate the investigated faults, which are applied to the system at different time intervals. Finally, the effectiveness of the proposed diagnostic approach is experimentally validated on an in-house setup of inverter-fed PMSMs.