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The feature extraction and state recognition of vibration signals of hydroelectric generating units are of great significance for effectively ensuring the safety and lifespan of unit operation. This paper introduces a novel fault diagnosis approach that leverages a wavelet threshold algorithm for initial signal preprocessing and enhances the Radial Basis Function (RBF) neural network with an optimized Gravitational Search Algorithm (GSA). Initially, the Wavelet Transform (WT) algorithm is employed to denoise the raw signal, which is subsequently decomposed using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). Intrinsic Mode Function (IMF) components are then selected based on their correlation coefficients for sample entropy calculation. A GSA-optimized RBF neural network is constructed to diagnose faults in hydroelectric units. To further enhance the model’s convergence speed and accuracy, Lévy flight and chaotic sequence optimizations are integrated into the GSA, resulting in the development of the LCGSA-RBF diagnosis model. Experimental results demonstrate that this method achieves a 100% accuracy rate, outperforming other comparative models. Specifically, this approach enhances accuracy by 12.5% over the CEEMDAN-RBF method, showing the highest alignment with actual fault diagnosis results. This research significantly contributes to the advancement of safe and reliable operations in hydroelectric generating units.

A novel operational risk assessment method for hydroelectric generating units (HGUs) is presented in this article. First, a multi-head spatio-temporal attention gated network (MSTAGN) is proposed to establish an operation risk benchmark model for HGUs to reveal the intricate relationship between performance and its multiple influencing factors. In particular, MSTAGN learns complex interaction relationships among multiple influencing factors in both temporal and spatial dimensions and automatically extracts important features. Then, a nonlinear mapping function is constructed to extract the deviation of the current measured performance parameters from the predicted baseline performance parameters as the operation risk degree. On this basis, an adaptive fuzzy clustering algorithm is proposed to achieve a clear classification of the operating risk level for HGUs. The proposed method is applied in a HGU in Sichuan province, China. The results of comparative experiments demonstrate its viability and efficacy. [ABSTRACT FROM AUTHOR]

In order to realize the automatic selection of rule number in fuzzy space partition in the identification of the Takagi–Sugeno (T–S) fuzzy model, a new fuzzy clustering method is proposed in this paper. With the purpose of searching for the optimal fuzzy cluster number and the corresponding cluster centers simultaneously, a novel improved hybrid backtracking search algorithm (IHBSA) has been proposed by introducing the idea of a hybrid encoding scheme as well as a variable valid length chromosome to this optimization algorithm. With a proper cluster validity index taken as the objective function, IHBSA is capable of partitioning the fuzzy space and identifying premise parameters of the T–S fuzzy model without setting a deterministic value of cluster number as a priori. Through the experimental analysis, it is demonstrated that the proposed method possesses higher approximation accuracy with relatively fewer rule number in comparison with traditional ones. Moreover, the T–S fuzzy model is implemented to the practical data of hydroelectric generating units (HGU), and preponderant trajectory matching performance has also been achieved when utilizing the proposed method. [ABSTRACT FROM AUTHOR]

This paper describes an adaptive multivariable control system for hydroelectric generating units. The system is based on a detailed mathematical model of the synchronous generator, the water turbine, the exiter system and turbine control servo. The models of the water penstock and the connected power system are static. These assumptions are not considered crucial. The system uses a Kalman filter for optimal estimation of the state variables and the parameters of the electric grid equivalent. The multivariable control law is computed from a Riccatti equation and is made adaptive to the generators running condition by means of a least square technique.