Zhongda Tian

Network traffic is an important indicator to evaluate the network status. The modeling and prediction of network traffic can effectively analyze and judge the future working status of the network. This study proposes a prediction model based on wavelet denoising (WD) and support vector machine (SVM) for small network traffic data samples. By using WD algorithm to denoise network traffic samples containing noise, the accuracy of subsequent modeling and prediction is improved. SVM is chosen as the prediction model for network traffic after denoising. An improved slime mould algorithm (SMA) with better performance is proposed to optimize the parameters of SVM. The performance of the designed improved SMA is validated using two benchmark functions. Two small sample network traffic datasets are used for simulation experiments. In the case of combining multiple performance indicators, the proposed prediction model is compared with multiple comparative prediction models. The results show that the proposed prediction model has high prediction performance and stable robustness, and has good prediction effect on small sample network traffic. Meanwhile, the training time and prediction time of the proposed model with sliding window mechanism are relatively short, which can meet the requirements for practical applications.

Fault diagnosis of wind turbines is essential for ensuring the safe and reliable operation of wind farms. However, Supervisory Control and Data Acquisition (SCADA) data are usually high-dimensional, redundant, and nonstationary, posing challenges to accurate classification and model generalization. To address these issues, this paper proposes a data-driven fault diagnosis framework. First, the k-nearest neighbors algorithm is applied to detect and remove anomalous samples, improving data quality. Then, a two-stage adaptive feature engineering (AFE) approach is developed: physics-informed features are designed to characterize intra-turbine thermal imbalance and coupling, and Light Gradient Boosting Machine (LightGBM) combined with permutation importance adaptively selects the most discriminative subset. An ensemble classifier integrating LightGBM, long short-term memory, and multilayer perceptron is then constructed, and the whale optimization algorithm is employed to fine-tune its hyperparameters. Experimental results on real SCADA data demonstrate that the proposed method significantly outperforms individual baseline models in both accuracy and robustness. Compared with the baseline LightGBM model, the optimized ensemble improves the accuracy from 0.9279 to 0.9913, with absolute gains of 6.34%, 6.23%, 7.27%, and 6.95% in accuracy, precision, recall, and F1-score, respectively, confirming its effectiveness and engineering applicability.

One of the fundamental aspects essential for ensuring the steadiness of wind power generation and the management of power systems is the accurate forecast of wind speed. We propose a short-term wind speed prediction model based on decomposition and bidirectional long short-term memory network. Firstly, the short-term wind speed is input into complete ensemble empirical mode decomposition of adaptive noise processing, which decomposes it into components with different local characteristic information to decrease the complexity of the wind speed pattern. Then, the bidirectional long short-term memory network with the attention mechanism is fitted with the decomposed data, and the particle swarm optimization algorithm is selected to optimize the hyper-parameters of bidirectional long short-term memory network to reduce the errors in modeling process. To derive the final prediction results, the forecasted values of each model output are added. The experimental results of two real short-term wind speed datasets verify that the designed approach has high accuracy in short-term wind speed forecasting, and its prediction values are better than other comparison models.

Delay Compensation Strategy of Networked Control System Based on Neural Network Predictive Control