To address the issue of rolling bearing fault diagnosis, this paper proposes a novel model combining the Improved Gorilla Troop Optimization (IGTO) algorithm, Variational Mode Decomposition (VMD), Permutation Entropy (PE), and Long Short-Term Memory (LSTM) networks. The IGTO algorithm is used to optimize the parameters of VMD and LSTM, enhancing signal decomposition and feature extraction. The proposed model achieves fault classification accuracies of 96.67% and 98.96% in the testing and training phases, respectively, on the Case Western Reserve University dataset, with minimal accuracy fluctuations. Furthermore, on the Jiangnan University dataset, the model reaches an average testing accuracy of 98.85%, with the highest accuracy reaching 99.48%. The results also demonstrate high stability, as indicated by low standard deviations (1.2148 and 1.3217) and narrow 95% confidence intervals ([95.75%, 97.58%] and [96.73%, 97.49%]). Despite a longer average runtime of 13.88 s per sample, the model’s superior accuracy justifies the computational cost. These results demonstrate the model’s excellent diagnostic performance, adaptability to different datasets, and practical applicability for rolling bearing fault diagnosis. This approach provides a valuable reference for predictive maintenance and fault detection systems in industrial applications.

Invertor as a virtual synchronous generator (VSG) to provide virtual inertia and damping can improve the stability of a microgrid, in which the damping is one of the fundamental problems in dynamics. From the view of the Hamiltonian dynamics, this paper researches the damping formation mechanism and damping injection control of VSG. First, based on the energy composition and dynamic characteristics of VSG, the differential equations system of VSG is established and is transformed into the generalized Hamiltonian system. Second, the effects of the three parameters of VSG, the damping coefficient D, active power droop coefficient, and time constant of excitation T_E on damping characteristics are researched from a dynamic perspective, and simulation research is carried out with an isolated microgrid. Lastly, the control design method of Hamiltonian structure corrections used to add the damping factor and design the equivalent control inject damping to improve the stability of the isolated microgrid. Research shows that the voltage and frequency stability of the isolated microgrid can be effectively improved by selecting three key parameters of VSG and damping injection control. The innovations of this paper are 1. The Hamiltonian model of the inverter is deduced and established by taking the inverter as a virtual generator. 2. Based on the Hamiltonian model, damping characteristics of inverter in the microgrid are studied. 3. Hamiltonian structure correction method is applied to the inverter, and equivalent damping injection is designed to improve the stability of the microgrid.

This study addresses the need for precise analysis and fault prediction in hydropower units by introducing an enhanced framework that integrates the Newton–Raphson-Based Optimizer (NRBO) with K-means clustering and random forest (RF) models. This framework dynamically optimizes both cluster numbers and hyperparameters, yielding significant improvements in clustering accuracy. The findings demonstrate that the NRBO-optimized K-means model achieves precise operational state classification without predefined cluster numbers, thereby enhancing intra-cluster compactness and inter-cluster separation. Experimental validation confirms the effectiveness of the proposed method in complex operational scenarios. Furthermore, the optimized RF model identifies critical features such as Active Power and Guide Vane Opening, which are crucial for risk detection under high-load conditions. This approach provides strong support for advanced state analysis and intelligent management of hydropower units, highlighting substantial potential for real-world applications.

To investigate the hydrodynamic noise characterization of hydraulic turbines with runner blade defects, this article establishes the intact machine model and three kinds of models with runner blade defects. Using the Computational Fluid Dynamic (CFD) and Computational Acoustic (CA) hybrid simulation computational methods, the hydrodynamic noise field of the hydraulic turbine is numerically simulated, and the results of the acoustic near field and acoustic far field are shown. 1. The double-row leaf grille and the runner are the primary sound source areas of the hydraulic turbine, and the intensity of sound radiation from these areas is positively correlated with the degree of runner blade defects. 2. As the runner blade defects develop, the sound power level (SWL) increases more significantly in the guide vanes near the nose of the spiral case in the double-row leaf grille. The most pronounced increase in the SWL is observed at the defective craters on the runner blades. 3. The frequency of the defective noise signal is primarily concentrated in the low-frequency band. The dominant frequency amplitude associated with runner blade defects increases and rises after the occurrence of defects. Secondary frequency changes are also observed, and the location of these changes varies at different receiving points.