Digital twin is one of the emerging technologies for the digital transformation of the power industry. Many existing studies claim that the widespread application of digital twins will shift the industry to a principally new level of development. This article provides an extensive overview of the industrial application experience of digital twin technologies for solving the problems of modern power systems with a particular focus on the task of high-voltage power equipment lifecycle management. The latter task contours one of the most promising areas for the application of the digital twins in the power industry since it requires deep analysis of the technological processes dynamics and the development of physical, mathematical and computer models that cover all the potential benefits of the digital twin technology. At the moment, there is a lack of reliable data on the problems of assessing and predicting the technical state of high-voltage power equipment. The use of digital twin technology in modern power systems will allow for aggregating data from a variety of real objects and will allow the automatization of collecting and processing of big data by implementing artificial intelligence methods, which will ultimately make it possible to manage the life cycle of the power equipment. The article puts to scrutiny the industrial experience of digital twins creation, considering the technical solutions suggested by the largest manufacturers of electrical equipment. A classification of digital twins, examples and main features of their application in the power industry, including the problem of managing the life cycle of high-voltage electrical equipment, are considered and discussed.

The paper addresses the problem of short-term renewable energy forecasting. The stochastic nature of renewable energy sources affects the power system planning procedures, subsequently reducing the reliability and security of power supply for final customers. The authors propose a machine learning approach for effective day-ahead forecasting using retrospective metering data and open source weather information provided by meteorological services. The paper addresses the problem of feature identification and appropriate error metrics. For the purpose of effective testing the user interface was created and tested on the real solar power plant situated in southern region of Russian Federation. The mean forecasting accuracy was obtained to be about 93%.

This paper addresses the problem of instrument current transformers technical state assessment based on machine learning methods. The introductory parts of the paper provide a detailed analysis of modern methods and approaches for technical state assessment of high-voltage power equipment of power plants and substations as well as a review of modern software tools and the latest trends in the given field of study. Justification of the relevance of the presented research aimed at instrument current transformers technical state assessment is provided along with the motivation for machine learning methods application for improvement of the accuracy and quality of high-voltage equipment state classification. Within the framework of the study, a comparative analysis of gradient boosting on decision trees and random forest algorithms was carried out for a given mathematical problem formulation. The main stages of processing the initial dataset are proposed as a step-by-step procedure, including feature extraction, feature transformation, feature interactions, etc. The outperforming efficiency of gradient boosting on decision trees algorithm was validated for real power equipment fleet. The resulting classification quality metrics of current transformers technical state assessment, Precision and Recall, are estimated to be 87.1% and 83.7%, correspondingly.

This paper is devoted to improving the assessment of the technical state (health index) of power circuit breakers through the use of machine learning, which allows to formalize expert knowledge, to identify implicit correlations and dependencies, and to automate the decision-making process under uncertainty. The paper proves the possibility of using machine learning based on decision trees to solve the task of assessing the power circuit breakers technical state and solves the problem of determining its optimal parameters and the training sample. The authors of the study carried out approbation of the developed model for assessing the technical state of breakers based on analyzing a large area of electrical networks in the Sverdlovsk Region, the Russian Federation, which proved its efficiency. The necessity of taking into account short-circuit currents when recognizing the pattern of the power breakers technical state is substantiated.

The load forecasting of a coal mining enterprise is a complicated problem due to the irregular technological process of mining. It is necessary to apply models that can distinguish both cyclic components and complex rules in the energy consumption data that reflect the highly volatile technological process. For such tasks, Artificial Neural Networks demonstrate advanced performance. In recent years, the effectiveness of Artificial Neural Networks has been significantly improved thanks to new state-of-the-art architectures, training methods and approaches to reduce overfitting. In this paper, the Recurrent Neural Network architecture with a small-size model was applied to the short-term load forecasting of a coal mining enterprise. A single recurrent model was developed and trained for the entire four-year operational period of the enterprise, with significant changes in the energy consumption pattern during the period. This task was challenging since it required high-level generalization performance from the model. It was shown that the accuracy and generalization properties of small-size recurrent models can be significantly improved by the proper selection of the hyper-parameters and training method. The effectiveness of the proposed approach was validated using a real-case dataset.

Forecasting the generation of solar power plants (SPPs) requires taking into account meteorological parameters that influence the difference between the solar irradiance at the top of the atmosphere calculated with high accuracy and the solar irradiance at the tilted plane of the solar panel on the Earth’s surface. One of the key factors is cloudiness, which can be presented not only as a percentage of the sky area covered by clouds but also many additional parameters, such as the type of clouds, the distribution of clouds across atmospheric layers, and their height. The use of machine learning algorithms to forecast the generation of solar power plants requires retrospective data over a long period and formalising the features; however, retrospective data with detailed information about cloudiness are normally recorded in the natural language format. This paper proposes an algorithm for processing such records to convert them into a binary feature vector. Experiments conducted on data from a real solar power plant showed that this algorithm increases the accuracy of short-term solar irradiance forecasts by 5–15%, depending on the quality metric used. At the same time, adding features makes the model less transparent to the user, which is a significant drawback from the point of view of explainable artificial intelligence. Therefore, the paper uses an additive explanation algorithm based on the Shapley vector to interpret the model’s output. It is shown that this approach allows the machine learning model to explain why it generates a particular forecast, which will provide a greater level of trust in intelligent information systems in the power industry.