Series capacitor compensation is used to improve the utilization of existing power systems. Subsynchronous resonance (SSR) can be caused by series compensated lines, which would lead to turbogenerator shaft breakdown. A novel approach was presented in this paper to analyze the characteristics and influence factors of SSR in a series compensated transmission system. The system model of SSR, including the various modules of the electromechanical network, was established, and the eigenvalue results under 70% series compensation level were analyzed by eigenvalue analysis method for the the institute of electrical and electronics engineers (IEEE) first benchmark model (FBM). Compared with the results of the power systems computer-aided design (PSCAD) modeling and simulation, the effectiveness of eigenvalue analysis method was proven. After that, the eigenvalue analysis method was used to study, in detail, the effects of system series compensation levels, synchronous generator parameters, speed governing system parameters, and excitation system parameters on SSR characteristics. The research results show that the series compensation level has the greatest influence on the torsional mode damping of the system. The parameters of generator reactance, speed governing system, and excitation system have some effect on the torsional mode damping. The parameters of excitation system significantly affect the low-frequency oscillation damping.

Valve internal leakage in thermal power stations exhibits a strong concealed nature. If it cannot be discovered and predicted of development trend in time, it will affect the safe and economical operation of plant equipment. This paper proposed an intelligent identification method for valve internal leakage that integrated an Improved Kepler Optimization Algorithm (IKOA) with Support Vector Regression (SVR). The Kepler Optimization Algorithm (KOA) was improved using the Sobol sequence and an adaptive Gaussian mutation strategy to achieve self-optimization of the key parameters in the SVR model. A multi-step sliding cross-validation method was employed to train the model, ultimately yielding the IKOA-SVR intelligent identification model for valve internal leakage quantification. Taking the main steam drain pipe valve as an example, a simulation case validation was carried out. The calculation example used Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and determination coefficient (R²) as performance evaluation metrics, and compared and analyzed the training and testing dataset using IKOA-SVR, KOA-SVR, Particle Swarm Optimization (PSO)-SVR, Random Search (RS)-SVR, Grid Search (GS)-SVR, and Bayesian Optimization (BO)-SVR methods, respectively. For the testing dataset, the MSE of IKOA-SVR is 0.65, RMSE is 0.81, MAE is 0.49, and MAPE is 0.0043, with the smallest values among the six methods. The R² of IKOA-SVR is 0.9998, with the largest value among the six methods. It indicated that IKOA-SVR can effectively solve problems such as getting stuck in local optima and overfitting during the optimization process. An Out-Of-Distribution (OOD) test was conducted for two scenarios: noise injection and Region-Holdout. The identification performance of all six methods decreased, with IKOA-SVR showing the smallest performance decline. The results show that IKOA-SVR has the strongest generalization ability and robustness, the best effect in improving fitting ability, the smallest identification error, the highest identification accuracy, and results closer to the actual value. The method presented in this paper provides an effective approach to solve the problem of intelligent identification of valve internal leakage in thermal power station.

Gearbox is one of the key components of wind turbine, whose fault will directly affect the safety and the operation of the overall wind turbine. This study develops a fault diagnosis method of wind turbine planetary gearbox. Using the order resampling method, the original non‐stationary time‐domain vibration signal is converted into a signal having a smooth angular domain or quasi‐stationary characteristics. The angle domain signal is conducted by empirical mode decomposition method. According to correlation coefficient criteria, the information mode function that contains fault feature is selected for signal reconstruction. The reconstructed signal is carried on by spectrum analysis. According to the classification of planetary gearbox, the fault feature parameters are extracted. J‐divergence and KL‐divergence (Kullback–Leibler divergence) calculated through the fault feature parameters are used to determine the fault location and describe the severity degree of the fault. Results of the experiments have revealed that the proposed method can be effective and accurate to the fault diagnosis of wind turbine planetary gearbox.

Accurate trend prediction of valve internal leakage is crucial for the safe and economical operation of thermal power units. To address the issues of prediction lag and insufficient accuracy in existing methods when dealing with the dynamic changes in internal leakage, this paper proposed an Improved Autoregressive Integrated Moving Average–Generalized Autoregressive Conditional Heteroskedasticity (IARIMA-GARCH) method that integrated Multi-Time-Scale Decomposition, an Improved ARIMA (IARIMA) model, and an Improved GARCH (IGARCH) model for accurate prediction of drain valve internal leakage. First, using a Multi-Time-Scale Decomposition method based on sampling at different time intervals, the original valve internal leakage time series were reconstructed into three characteristic subsequences—short-term, medium-term, and long-term—to capture the evolutionary features at various time scales. Then, an IARIMA model, employing the Huber loss function for robust parameter estimation, was constructed as the leakage prediction model to effectively suppress the interference of outliers. Simultaneously, an IGARCH model was built as the leakage volatility prediction model by introducing the previous moment’s volatility to correct the current residual, establishing a feedback mechanism between the mean and volatility equations, thereby enhancing the characterization of volatility clustering. Finally, using a weight coefficient dynamic calculation method based on RMSE, the Multi-Time-Scale prediction results of each subsequence were fused to obtain the final predicted valve internal leakage. Taking the main steam drain valve of a thermal power plant as the research object, and using Mean Absolute Error (MAE), Root-Mean-Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and symmetric Mean Absolute Percentage Error (sMAPE) as evaluation metrics, a case study on trend prediction of drain valve internal leakage was conducted, comparing the proposed method with ARIMA, Long Short-Term Memory networks (LSTM) and eXtreme Gradient Boosting (XGBoost) methods. The results showed that compared to ARIMA, LSTM and XGBoost, the proposed IARIMA-GARCH method achieved the lowest values on error metrics such as Mean Absolute Error (MAE), Root-Mean-Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and symmetric Mean Absolute Percentage Error (sMAPE), and its Coefficient of Determination (R²) is closest to 1. The standardized residual sequence most closely resembled a white noise sequence with zero mean and unit variance, and its distribution was the closest to a normal distribution. This proved that the IARIMA-GARCH method possessed higher prediction accuracy, stronger dynamic adaptability, and superior statistical robustness, providing an effective solution for valve condition prediction and predictive maintenance.

To address the problems of local optima and insufficient convergence accuracy in parameter identification of primary frequency regulation (PFR) for steam turbines, this paper proposed a hybrid identification method that integrated an Improved Bayesian Optimization (IBO) algorithm and an Improved Whale Optimization Algorithm (IWOA). By initializing the Bayesian parameter population using Tent chaotic mapping and the reverse learning strategy, employing a radial basis kernel function hyperparameter training mechanism based on the Adam optimizer and optimizing the Expected Improvement (EI) function using the Limited-memory Broyden–Fletcher– Goldfarb–Shanno with Bounds (L-BFGS-B) method, IBO was proposed to obtain the optimal candidate set with the smallest objective function value. By introducing a nonlinear convergence factor and the adaptive Levy flight perturbation strategy, IWOA was proposed to obtain locally optimized optimal solutions. By using the reverse-guided optimization mechanism and employing a fitness-oriented selection strategy, the optimal solution was chosen to complete the closed-loop process of reverse learning feedback. Nine standard test functions and the Proportional Integral Derivative (PID) parameter identification of the electro-hydraulic servo system in a 330 MW steam turbine were presented as examples. Compared with Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Bayesian Optimization (BO) and Particle Swarm Optimization-Grey Wolf Optimizer (PSO-GWO), the Improved Bayesian Optimization-Whale Optimization Algorithm (IBO-WOA) proposed in this paper has been validated to effectively avoid the problem of getting stuck in local optima during complex optimization and has high parameter recognition accuracy. Meanwhile, an Out-Of-Distribution (OOD) Test based on noise injection had demonstrated that IBO-WOA had good robustness. The time constant identification of the steam turbine were carried out using IBO-WOA under two experimental conditions, and the identification results were input into the PFR model. The simulated power curve can track the experimental measured curve well, proving that the parameter identification results obtained by IBO-WOA have high accuracy and can be used for the modeling and response characteristic analysis of the steam turbine PFR.