Changes in external conditions, parameters of object functioning, relationships between system elements and system connections with the supersystem lead to a decrease in the accuracy of the artificial intelligence models results, which is called model degradation. Reducing the risk of model degradation is relevant for electric power engineering tasks, the peculiarity of which is multifactor dependencies in complex technical systems and the influence of meteorological parameters. Therefore, automatic updating of models over time is a necessary condition for building user confidence in forecasting systems in power engineering tasks and industry implementations of such systems. There are various methods used to prevent degradation, including an algorithm for detecting data drift, an algorithm for updating models, their retraining, additional training, and fine-tuning. This article presents the results of a study of drift types, their systematization and classification by various features. The solution options that developers need to make when creating intelligent forecasting systems to determine a strategy for updating forecast models are formalized, including update trigger criteria, model selection, hyperparameter optimization, and the choice of an update method and data set formation. An algorithm for forming a strategy for automatic updating of artificial intelligence models is proposed and practical recommendations are given for developers of models in problems of forecasting time series in the power industry, such as forecasting electricity consumption, forecasting the output of solar, wind and hydroelectric power plants.

Keywords: time series forecasting, artificial intelligence, machine learning, trusted AI system, model degradation, data drift, concept drift

The reliability of electric power systems is largely determined by the insulation condition of electrical equipment. Insulation damage can lead to power losses, reduced service life of lines and devices, and emergency shutdowns, so insulation diagnostics is critical to prevent technological disruptions. However, traditional approaches to insulation monitoring are often labor-intensive and subjective. In this regard, the role of computer vision and deep learning methods, capable of automatically detecting insulation defects and thereby increasing the efficiency and objectivity of monitoring, is increasing. This study considers the application of modern architectures of deep convolutional neural networks for the problem of recognizing insulating elements of electrical equipment. Particular attention is paid to the comparative analysis of several state-of-the-art models. The considered architectures show effective results and provide deep multi-scale analysis of scene features based on convolutional networks. In this paper, the models are used in conjunction with image augmentation algorithms. Data augmentation allows you to artificially expand limited sets of training images through various transformations, which is especially important for a small dataset. The application of these methods is aimed at improving the quality of training data and reducing the risk of overfitting models, as well as overcoming the imbalance of classes in the sample by generating additional fault samples. The proposed approach includes conducting a sequential comparative experiment on a small and limited set of image data from power facilities. A comparison was made of the accuracy and completeness metrics of various neural network architectures when combining various augmentation strategies in order to identify a combination of models and data augmentation methods that provide the highest recognition accuracy. The results of the study will help determine the most effective augmentation models and techniques suitable for real-life operating conditions at power facilities, taking into account complex backgrounds, variable lighting, and different angles of equipment shooting. Identifying such optimal solutions based on deep learning is intended to improve the reliability and efficiency of automated insulation monitoring in the power industry.

Keywords: computer vision, convolutional neural networks, isolation, defect, data augmentation, machine learning, energy, automation of image analysis