To enhance the operational efficiency and fault detection accuracy of hydroelectric units, this paper proposes a parallel convolutional neural network model that integrates the Gramian angular summation field (GASF) with an Improved coati optimization algorithm–parallel convolutional neural network (ICOA-PCNN). Additionally, to further improve the model’s accuracy in fault identification, a multi-head self-attention mechanism (MSA) and support vector machine (SVM) are introduced for a secondary optimization of the model. Initially, the GASF technique converts one-dimensional time series signals into two-dimensional images, and a COA-CNN dual-branch model is established for feature extraction. To address the issues of uneven population distribution and susceptibility to local optima in the COA algorithm, various optimization strategies are implemented to improve its global search capability. Experimental results indicate that the accuracy of this model reaches 100%, significantly surpassing other nonoptimized models. This research provides a valuable addition to fault diagnosis technology for modern hydroelectric units.

Calculation of the hydraulic coupling in the single penstock multi‐machine (SPMM) is one of the main obstacles for control design of the hydro turbine governor. In order to resolve this problem, a multi‐machine differential equation model suitable for control design and stability analysis is established in this study. First, the multi‐machine differential equation with the hydraulic coupling model is then established to facilitate building the joint model of the hydro turbine with the hydraulic system and the generator considering the hydraulic‐mechanic‐electric coupling. Second, the dynamic head in the common penstock satisfying the superposition principle is revealed. This means that the dynamic head in the common penstock can be calculated by using the state variables of the branch pipe. Third, simulation reveals that the differential item in the hydraulic coupling is the main factor affecting the computational convergence. As such, the approximate method ignoring the differential item in the hydraulic coupling is proposed to solve the problem of computational convergence of the SPMM. Finally, the classical method of characteristic (MOC) is employed to verify the proposed model. The results show that the proposed model has higher accuracy and is easy to connect with the non‐linear model of the generator.

The converter is an important component of a wind turbine, and its control system has a significant impact on the dynamic output characteristics of the wind turbine. For the double-fed induction generator (DFIG) converter, the control parameter identification method is proposed. In this paper, a detailed dynamic model of DFIG with the converter is built, and the trajectory sensitivity method is used to study the observation points that are sensitive to the change of control parameters as the observation quantity for control parameter identification; the Whale Optimization Algorithm (WOA) is used to study the converter control system parameters that dominate the output characteristics of DFIG in the dynamic full-process simulation. To validate the proposed method, four classical test functions are used to verify the effectiveness of the algorithm, and the control parameters are identified by setting a three-phase grounded short-circuit fault under maximum power point tracking (MPPT), and the identification results are compared with particle swarm optimization (PSO) and chaotic particle swarm optimization (CPSO) to show the superiority of the proposed method. The final results show that the proposed WOA can identify the control system parameters faster and more accurately.