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Currently, diagnostic methods for compound fault of mixed eccentricity and demagnetization (CFMED) in external rotor permanent magnet motors (ERPMMs) still face the challenges of overlapping fault features and scarce training data. Therefore, a new data-driven diagnosis method is proposed for CFMED in ERPMMs in this article. First, an analytical model (AM) of back electromotive force (EMF) of ERPMMs with CFMED is proposed. Then, the bispectrum of back EMF with CFMED is analyzed. Furthermore, based on the proposed AM, large-sample CFMED datasets is efficiently established. Based on the convolutional neural network, a two-step diagnosis model of CFMED is established. Finally, experimental prototypes for simulating CFMED are made, the validity and robustness of the proposed diagnosis model are verified. The proposed method provides a new idea for the diagnosis of CFMED.

Multirate (MR) partitioning is emerging as a promising technique for improving the performance of complex power systems with limited computing resources. However, the modeling and stability evaluation methods of such MR power systems have not been fully investigated, which limits their wider application. An iteratively-nested state-space (INSS) modeling method is proposed in this article to improve the stability analysis accuracy of the MR power system. System partitions, with different discrete time-steps, are modeled in state-space and iteratively-nested with each other to form a single-rate system, the stability of which can then be evaluated with high accuracy based on the eigenvalue analysis. The technique of fractional sum of the power matrix is adopted for derivation when the discrete time-steps of the MR system are not integer multiples of each other. The modeling accuracy of the proposed method is higher than that of the linear averaging-based solution, while the modeling complexity is also greatly reduced compared with that of the lifting-based solution. A case study of a triple-rated power hardware-in-the-loop (PHIL) system is performed as an example to demonstrate the utility of the proposed INSS modeling method. Moreover, the presented modeling process is in a generalized form, and can be easily applied to the modeling and optimization of MR real-time simulation, MR digital-twin deployment, and MR process control for its engineering feasibility.

An electric motor emulator (EME) is being prevalently used during the testing process of the motor drive unit for electric vehicle applications. Most of the existing research works focus on the improvement and analysis of the terminal current emulation accuracy of the EME, whereas research on the emulation accuracy of the EME terminal voltage is rare. The purpose of this article is to cope with the distortion of the EMEs terminal voltage at high modulation index due to the parasitic capacitance and deadtime of the emulator converter. A novel control scheme based on an active circulation regulation approach and a switched-mode modulation strategy is proposed in this article. The proposed control scheme is aimed at the commercial EME with the parallel-branch converter. Both simulations and experiments are carried out based on a prototype power topology, validating the effectiveness of the proposed control scheme in improving the voltage emulation accuracy of the EME.

Electric motor emulator (EME) is being increasingly utilized during the development and test process of motor drive units (MDUs) for electric vehicle applications. However, the high-speed instability issue, without sufficient corresponding theoretical analysis currently, limits its further application. Hence, a comprehensive modeling and stability analysis approach for the high-speed EME is presented here to enrich the understanding of this special power hardware-in-the-loop testing scenario. A detailed model of the EME testing system with MDU included is developed in complex vector (CV) form for the first time, the poles loci of which are then plotted for accurately predicting the system stability. Influences of various factors including control bandwidth, time delay, and control structure on the speed limit of the EME are investigated, with parameter nonlinearities in the motor model also considered to demonstrate the extended applicability of the CV-based modeling approach. The maximum error between the predicted speed limits and the experimental results is less than 20 Hz, and the EME running up to 800 Hz is also experimentally achieved by adopting the internal model control structure, both validating the effectiveness and accuracy of the presented CV modeling and stability analysis approach for the high-speed EME.

In this article, a new data-driven diagnosis method is proposed for mixed eccentricity (ME) in the external rotor permanent magnet motors (ERPMMs). Differently from the previous articles, the proposed method has the advantages of large sample size, high sample diversity, high efficiency, and strong generalization ability. First, the improved parametric analytical model (AM) of back electromotive force (EMF) of the ERPMMs is established. Then, the characteristics of the back EMF are analyzed. Accordingly, its amplitudes of fundamental waves and sideband harmonics are selected as the ME indexes. Afterwards, by using the proposed parametric AM, a database of ME signals is established efficiently, which contains tens of thousands of labeled samples. Furthermore, based on the back propagation neural network, a high-precision diagnosis model for ME in the ERPMM is established. Interference faults, such as unbalanced stator windings and uneven magnetization are also discussed. Finally, an experimental prototype for simulating the ME is manufactured and the effectiveness of the proposed method is verified. The maximum absolute diagnostic error is less than 4.0%. It provides a new idea for multiparameter diagnosis for ME.