This paper develops an online inter-turn fault diagnosis method for permanent magnet synchronous machine (PMSM). The mathematical model of the PMSM with inter-turn fault is established. The zero sequence voltage component and zero sequence current component are analyzed in the PMSM, respectively. Then the new fault indicators are defined to remove the influence of the variation of the rotor speed and an effective frequency tracking algorithm is presented to extract fault indicators. In this proposed method, not only the inter-turn fault can be effectively detected, but also the phase in which this fault occurs can be accurately identified. The experiments are carried out and the experiment results verify the effectiveness of the proposed method.Index Terms-Fault diagnosis, frequency tracking, permanent magnet machine, zero sequence component.
Faults occurring in wind turbine (WT) degrade the performance and efficiency of wind power generation system, which result in large operation and maintenance cost of the WT. Therefore effective fault diagnosis schemes are greatly required for WT. This study presents a multi-sensors information fusion technology for fault diagnosis of the WT, where vibration sensors are adopted. Firstly, to deal with the non-stationary nature of the vibration signals of the WT, empirical mode decomposition method is utilised to extract fault features from the signals. Secondly, different classifiers are trained separately to classify fault types based on independent feature vectors from different sensors. Finally, three fusion approaches, named ordered weighted averaging, D-S evidential reasoning and fuzzy integral, are, respectively, used for fusing the diagnosis results of individual classifiers. To validate the proposed method, a direct-drive WT test rig is constructed and the related experiments are carried out. The experimental results validate the assumption that the proposed approach is effective for fault diagnosis of the WT, which has a higher diagnostic accuracy than that of individual sensor.
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