Considering the volatility and randomness of wind speed, this research suggests an improved hunter-prey optimization (IHPO) algorithm-based extreme learning machine (ELM) short-term wind power prediction model to increase short-term wind power prediction accuracy. The original wind power history data from the wind farm are used in the model to achieve feature extraction and data dimensionality reduction, using the partial least squares’ variable importance of projection (PLS-VIP) and normalized mutual information (NMI) methods. Adaptive inertia weights are added to the HPO algorithm’s optimization search process to speed up the algorithm’s convergence. At the same time, the initialized population is modified, to improve the algorithm’s ability to perform global searches. To accomplish accurate wind power prediction, the enhanced algorithm’s optimal parameters optimize the extreme learning machine’s weights and threshold. The findings demonstrate that the method accurately predicts wind output and can be confirmed using measured data from a wind turbine in Inner Mongolia, China.

Accurate short-term load forecasting can ensure the safe and stable operation of power grids, but the nonlinear load increases the complexity of forecasting. In order to solve the problem of modal aliasing in historical data, and fully explore the relationship between time series characteristics in load data, this paper proposes a gated cyclic network model (SSA–GRU) based on sparrow algorithm optimization. Firstly, the complementary sets and empirical mode decomposition (EMD) are used to decompose the original data to obtain the characteristic components. The SSA–GRU combined model is used to predict the characteristic components, and finally obtain the prediction results, and complete the short-term load forecasting. Taking the real data of a company as an example, this paper compares the combined model CEEMD–SSA–GRU with EMD–SSA–GRU, SSA–GRU, and GRU models. Experimental results show that this model has better prediction effect than other models.

The purpose of this study was to better apply artificial intelligence algorithm to load forecasting and effectively improve the forecasting accuracy. Based on the long short-term memory neural networks, a combined model based on whale bionic optimization is proposed for short-term load forecasting. The whale bionic algorithm is used to solve the problem that the long short-term memory neural networks are easy to fall into local optimization and improve the accuracy of parameter optimization. The original signal is decomposed into multiple characteristic components by set empirical mode decomposition. Each feature component is input into the bionic optimized combination model for prediction. Finally, get the load forecasting results. Compared with the prediction results of EEMD-ARMA model, RNN model, LSTM model, and WOA-LSTM model, the combined prediction model optimized by whale bionics has less prediction error and higher prediction accuracy.

The rapid development of the automobile industry has made life easier for people, but traffic accidents have increased in frequency in recent years, making vehicle safety particularly important. This paper proposes an improved YOLOv5s algorithm for vehicle identification and detection to reduce vehicle driving safety issues based on this problem. In order to solve the problems of a disappearing model training gradient in the YOLOv5s algorithm, difficulty in recognizing small objects and poor recognition accuracy caused by the boundary frame regression function, it is necessary to implement a new function. These aspects have been enhanced in this article. On the basis of the traditional YOLOv5s algorithm, the ELU activation function is used to replace the original activation function. The attention mechanism module is then added to the YOLOv5s algorithm’s backbone network to improve the feature extraction of small and medium-sized objects. The CIoU Loss function replaces the original regression function of YOLOv5s, thereby enhancing the convergence rate and measurement precision of the loss function. In this paper, the constructed dataset is utilized to conduct pertinent experiments. The experimental results demonstrate that, compared to the previous algorithm, the mAP of the enhanced YOLOv5s is 3.1% higher, the convergence rate is 0.8% higher, and the loss is 2.5% lower.

The insufficient amount of sample data and the uneven distribution of the collected data across faults are key factors limiting the application of machine learning in power transformer fault warning, as demonstrated by the poor adaptability of the established data-driven models under actual operating conditions. In this paper, an unsupervised and supervised learning method is designed for power transformer fault early warning based on electrical quantities and vibration signals. The method is based on the Fourier levels of transformer vibration signals under different electrical conditions measured in the field, and the vibration features are clustered according to their intrinsic properties by means of a spectral clustering algorithm. A decision tree model of the vibration characteristics under each cluster is then constructed to calculate early warning values for the transformer vibration spectrum under different electrical conditions, enabling the assessment of transformer production variability. The above process, which is based on field measurement data and data mining analysis methods, is cheaper than the existing transformer fault warning techniques at home and abroad and makes better use of information and training models.

A running transformer frequently experiences interturn faults; they are typically difficult to detect in their early stages but eventually progress to interturn short circuits, which cause damage to the transformer. Therefore, finding out the fault mechanism of the full interturn fault process can provide a theoretical basis for transformer fault detection. In this paper, an electromagnetic-solid mechanics coupled finite element model consistent with an actual oil-immersed three-phase transformer is established. The transient process of winding from interturn discharge to interturn short circuit is simulated to study the electromagnetic characteristics as well as the mechanical characteristics during transformer failure. The model parameters of the transformer are simulated to obtain the fault current, electromagnetic parameters and other performance parameters to study the characteristics of the magnetic field and coil force when interturn faults occur. Finally, the vibration of the transformer casing is used to detect as well as diagnose the transformer fault situation, providing a theoretical basis for the study of transformer detection and diagnosis capability improvement measures.