THE USE OF ARTIFICIAL INTELLIGENCE FOR AUTOMATING THE PROCESS OF FREQUENCY LOAD SHEDDING IN NETWORKS

Abstract

This article aims to explore the experience of using artificial intelligence to solve the problem of predicting frequency load shedding in networks. This allows for the automation of this process and eliminates errors made by human operators. The development of automatic forecasting for the period of using frequency load shedding correlates with the regulatory documents on the digital transformation and digitalization of Ukraine, making the research relevant and practically significant.

A schematic of the process for using artificial intelligence to predict frequency load shedding has been developed. The foundation for building an effective artificial intelligence model is the availability of an extensive and high-quality database on the energy sector in a specific region. This database must include historical data on network load, weather conditions, technological parameters of equipment operation, and other factors influencing the frequency characteristics of the power system. It is this data that serves as the fuel for training machine learning algorithms, enabling them to detect complex, non-obvious patterns and correlations that are elusive to humans.

The implementation of such a system involves several key stages. First, it is the collection and pre-processing of data, which includes cleaning the information and detecting anomalies. Second, it is the direct construction and training of the model, for example, based on regression analysis methods, time series analysis, or even complex neural networks. Third, it is the integration of the model into the dispatch toolkit to provide operational recommendations to operators. The implementation of such intelligent systems allows not only for increasing the stability of the power grid but also for significantly optimizing its maintenance costs, transitioning from reactive emergency elimination to proactive management of operating modes. Thus, the automation of forecasting using artificial intelligence is an important step in building a modern, resilient, and digital energy infrastructure for Ukraine.