Junqing Wang

Electromagnetic force is an important excitation source of stator-core vibration in hydro-generators. The methodological novelty of this study lies in an order-specific spectral-attribution framework for electromagnetically induced stator vibration. Unlike conventional electromagnetic–structural analyses that focus mainly on response-amplitude prediction or resonance screening, the proposed framework links electromagnetic-field distribution, Maxwell stress-based force harmonics, experimentally validated modal frequencies, swept-frequency harmonic response, and normalized modal detuning to distinguish force-dominated components from components influenced or amplified by nearby structural modes. A full-scale three-dimensional electromagnetic model of an actual hydro-generator was established to calculate the electromagnetic surface force acting on the stator-core tooth surfaces. The electromagnetic-force spectrum was obtained using fast Fourier transform. The steady-periodic surface-force data were converted into complex harmonic load vectors through Maxwell–Mechanical coupling and mapped onto the structural model as distributed surface loads, while hammer-impact modal tests were used to validate the structural dynamic model. With an electrical fundamental frequency of 50 Hz, the force spectrum contained components from approximately 49.9 to 499.2 Hz, corresponding to the first ten electrical-frequency orders. The principal vibration responses occurred near 9.98, 99.78, 199.56–201.55, 299.33, and 399.11–399.16 Hz. The minimum modal detunings of the 4th-, 6th-, and 8th-order electrical-frequency components were 4.02–4.97%, 1.81%, and 2.41–2.42%, respectively. Accordingly, the 4th-order component was identified as a dominant electromagnetic forced response with possible influence from the 210 Hz mode, the 6th-order component exhibited modal amplification associated with the 294 Hz mode, and the 8th-order component was influenced by the nearby 409 Hz mode. Although the 9th- and 10th-order force components were present, their vibration responses remained weak. These results demonstrate that the vibration spectrum is shaped jointly by electromagnetic excitation and structural frequency-response effects, providing a quantitative basis for electromagnetic-source reduction, structural detuning, and damping enhancement.

This paper proposes an efficient analytical modeling method for the air-gap flux density in hydro-generators under eccentricity faults. The method specifically addresses the multi-factor coupling characteristics induced by the combined effects of stator slotting, salient-pole rotor structure, and eccentricity. Based on geometric analytical derivation, individual models for air-gap length considering stator slotting, rotor salient-pole structure, and eccentricity are established, and an analytical expression for the air-gap length under coupled conditions is derived. By incorporating the spatial distribution characteristics of the magnetomotive force, a mathematical model for no-load air-gap flux density is constructed, enabling the rapid calculation of air-gap magnetic field distribution. Finite element verification conducted on an 84-slot, 10-pole hydro-generator demonstrates that the proposed method accurately reflects the periodic fluctuations and amplitude variations in the magnetic flux density under both normal and eccentric conditions. Compared with the finite element method (FEM), the analytical approach significantly enhances computational efficiency while maintaining high accuracy, providing effective theoretical support for the structural optimization and eccentricity fault diagnosis of hydro-generators.

To address the problems of low diagnostic accuracy and weak robustness in bearing fault diagnosis, this paper establishes a theoretical framework for frequency-domain-enhanced residual network diagnosis driven by a multi-stage adaptive snow goose algorithm (SGA), breaking through the fundamental bottleneck of structural rigidity in traditional methods. The dual-flexible residual network achieves adaptive matching between network topology and fault characteristics through variable convolutional layers and dynamic skip connections. The frequency-domain attention module, based on Fourier transform and magnitude-phase separation techniques, provides in-depth analysis of the frequency-domain discrimination mechanism in vibration signals. The improved snow goose algorithm integrates multi-stage adaptation, opposition-based learning, elite memory management strategies, and cooperative foraging mechanisms, enhancing the optimization performance of SGA. Validation on the Jiangnan University and Case Western Reserve University (CWRU) bearing fault datasets demonstrates that the proposed method achieves diagnostic accuracies of 99.81% and 99.78%, respectively, representing improvements of 4.62%, 6.41%, and 8.76% over mainstream methods such as ResNet, WDCNN, and Transformer. Furthermore, when faced with changes in data structure, the proposed method exhibits a performance degradation of only 0.43%, which is significantly better than other methods, demonstrating excellent robustness and cross-domain generalization capability, and it has certain practical significance.

Accurate identification of electromagnetically induced stator vibration frequency components is essential for the online condition monitoring of hydro-generators, particularly for assessing the dynamic state of the stator core under normal operating conditions. In engineering practice, the fast Fourier transform (FFT) is widely used for vibration spectrum analysis; however, because the measured vibration response is simultaneously affected by electromagnetic excitation, mechanical rotation, hydraulic disturbance, and external harmonic interference, FFT-based spectra often contain multiple frequency components whose structural relevance is difficult to determine directly. To address this issue, this paper proposes a coupled continuous wavelet transform and power spectral density transmissibility (CWT-PSDT) method for identifying key vibration frequency components with stable time-frequency energy and inter-sensor transmissibility in hydro-generator stator vibration signals. In the proposed framework, the analytic Morlet wavelet is first employed to localize dominant energy bands in the time-frequency domain, and PSDT is then used to screen frequency components with relatively stable inter-sensor transmissibility characteristics, thereby reducing the ambiguity caused by excitation-dominated spectral components. A clamped-clamped beam model is first used for numerical validation, and the maximum identification error of the first five natural frequencies is 4.22%. Experiments on a Francis turbine-generator test rig under five operating conditions further show that the proposed method can distinguish the mechanical rotational component near 10.3 Hz from the electromagnetic-related component near 50.8 Hz, while retaining higher-order electromagnetic-related components around 150 Hz and 250 Hz. The results demonstrate that the proposed CWT-PSDT method provides a physically interpretable and data-efficient approach for extracting stator-core-related spectral features, and offers a theoretical basis for spectrum-based online monitoring and future abnormal-condition comparison of hydro-generator stator responses.

The reliability of the generator circuit breaker (GCB) switching coil affects the safe and stable operation of the power system, in which the faults of abnormal voltage, poor contact, and mechanical jamming of the switching coil can easily lead to the refusal of the circuit breaker, which threatens the safety of the power grid. In order to study the fault characteristics of the GCB switching coil, this paper combines multi-physical field simulation and experimental testing, establishes the electromagnetic field simulation model of the switching coil, and analyzes the characteristics of current waveforms under typical faults such as voltage abnormality, poor contact, and core jamming. Through simulation and testing to verify the mechanism of current waveform distortion under different fault states, demonstrated the change rule of characteristic parameters when the fault occurs, and provided a basis for the diagnosis of the operation status of the switching coil based on current waveform.

This paper studies the collaborative inertia-frequency regulation strategies for the high renewable energy penetrated low inertia power system. Firstly, a systematic investigation is conducted to reveal the dominant dynamic characteristics and the possible challenges for such systems, and then proved the effectiveness of virtual inertia. Subsequently, a novel Laguerre-based model predictive control strategy is accordingly pro-posed, which ensures a better system states convergence ability and a reduced computational burden. The controller takes into account the system’s dual-mode feature to ensure timely response for both the inertia and the frequency support. Then, the regulation quality, operational burden and the cost are mathematically defined. The control trajectory is determined by the rolling optimization. The Gravity Searching Algorithm is utilized to determine the optimal control parameters. Finally, the proposed control strategy is validated through five case studies, demonstrating enhanced robustness, superior dynamic performance and cost-effective operation. This study provides new insights for the analysis and control strategies of the high RE penetrated low inertia systems.