This research investigates the detection of the detailed static air-gap eccentricity position (SAGE) at both ends of non-salient pole synchronous generators (SG). Differently from the previous studies, the detection method proposed does not necessitate the installation of invasive sensors inside the generator and instead it is based on two groups of symmetrically distributed search coils placed at both end-sides outside the generator. The root mean square (RMS) value comparison in electromotive forces (EMFs) of the search coils is then employed to assess the exact SAGE position of each end-side. Numerical 3D finite element calculation together with an experimental campaign on a two-pole 3000rpm CS-5 prototype synchronous generator validates the effectiveness of the proposed method. It is shown that the maximum assessment error by using four external search coils at each end is 15.1%, while by setting more coils the error can be significantly reduced. Since this proposed method has no need to install invasive search coils inside the generator, it is more convenient for field testing and helps improve the cost-performance of online SAGE condition monitoring.

This article presents a comprehensive analysis on the end winding vibration characteristics due to different stator inter-turn short circuit (SISC) positions in synchronous generators. Different from other studies, this article not only pays attention to the effect of SISC on the end winding electromagnetic forces, but also considers the impact of the short circuit position, which is related to the static air-gap eccentricity, on the end winding vibrations at the meantime. The whole work is based on the qualitatively theoretical analysis, the finite element analysis (FEA), and the experimental validation on a 6-pole synchronous generator. The study shows that SISC will considerably enlarge the electromagnetic force and intensify the radial vibration of the end winding, especially at 100 Hz, 200 Hz and 300 Hz. The closer the short circuit position is to the minimum air-gap point, the more severe the end winding vibration will be.

Periodic impacts are considered as the important defect signatures of wind turbine bearing. However, it is difficult to separate the weak periodic impacts from collected signal under serious interference environment. Facing with this issue, the recently developed signal processing technique named adaptive chirp mode decomposition (ACMD) is firstly introduced into the fault diagnosis field of wind turbine bearing. And the influences of instantaneous frequency and weighting coefficient parameters of ACMD are researched through simulated fault signal. In additional, the L-kurtosis fluctuation spectrum (LFS) guided instantaneous frequency selection strategy and the cuckoo search algorithm (CSA) guided weighting coefficient determination strategy are respectively put forward to automatically confirm the optimal influencing parameters without requiring priori knowledge. Combined with these two specific strategies, an innovation optimized adaptive chirp mode decomposition (OACMD) method is further proposed for defect identification of wind turbine bearing. The experimental signals and the engineering signal are in turn adopted to demonstrate the reliability of this proposed diagnostic method. The obtained analysis results show that the informative target mode with obvious periodic impact signatures can be effectively extracted, and the key defect characteristic frequencies are able to be successfully identified. Furthermore, the conventional spectral kurtosis method, the minimum entropy deconvolution method and the singular spectrum decomposition method are respectively employed for performance comparison. And it is validated that the proposed OACMD method has outstanding advantage on weak defect detection of wind turbine bearing.

Distribution line insulators play a key role in electrical insulation and supporting lines in distribution lines. Insulator defects due to overvoltage, thermal stress, and environmental pollution may trigger power transmission instability and line collapse, thus threatening the safe operation of distribution networks. However, distribution line insulators often present detection challenges due to their compact dimensions, diverse flaw types, and frequent installation in populated areas with visually cluttered environments. The combination of these factors, including small defect sizes, varying failure patterns, and complex background interference, in both urban and rural settings, creates significant difficulties for precise defect identification in these critical components. In response to these challenges, this paper proposes a defect recognition algorithm for distribution line insulators based on the improved YOLO11 model. Firstly, the algorithm combines the detection head of the original model with the Adaptively Spatial Feature Fusion (ASFF) module to effectively fuse defect features at different resolution levels and improve the model’s ability to recognize multi-scale defect features. Secondly, a Bidirectional Feature Pyramid Network (BiFPN) replaces the FPN + PAN structure of the original model to achieve a more effective transfer of contextual information in order to facilitate the model’s efficiency in performing defect feature fusion, and the Convolutional Block Attention Module (CBAM) Attention mechanism is embedded in the BiFPN output so that the model is able to give priority attention to defective features on insulators in complex recognition environments. Finally, the ShuffleNetV2 module is used to reduce the parameters of the improved model by replacing the large-parameter C3k2 module at the end of the backbone network for easy deployment on lightweight and small devices. The experimental results show that the improved model performs well in the distribution line insulator defect detection task, with an accuracy precision (AP) and mean accuracy precision (mAP) of 97.0% and 98.1%, respectively, which are 1.4% and 0.7% higher than the original YOLO11 model.

The conventional sliding mode observer (SMO) employed in the sensorless control for the permanent magnet synchronous machine (PMSM) has the problems of chattering phenomena and phase delay, which results in large errors between the estimated and actual values of the rotor speed and position. Thus, this paper proposes an improved SMO based on fuzzy logic controller (FLC) and dual second-order generalized integrator-frequency-locked loop (DSOGI-FLL) structure. The FLC is used to adjust the parameters of the SMO to improve the adaptive ability of the system and reduce the chattering. At the same time, in order to reduce the adverse effects from low-pass filter (LPF), DSOGI-FLL is used to accurately extract the back electromotive force (back-EMF) and effectively eliminate the high-order harmonic components. Finally, the proposed method is verified by simulation and experiment, and compared with a novel super-twisting algorithm-base SMO with a complex vector generalized integrator (STASMO-CVGI), the results show that the improved SMO has better performance.