Review of Methods for Diagnosing the Degradation Process in Power Units Cooperating with Renewable Energy Sources Using Artificial Intelligence

Abstract

This work is based on a literature review (191). It mainly refers to two diagnostic methods based on artificial intelligence. This review presents new possibilities for using genetic algorithms (GAs) for diagnostic purposes in power plants transitioning to cooperation with renewable energy sources (RESs). The genetic method is rarely used directly in the modeling of thermal-flow analysis. However, this assignment proves that the method can be successfully used for diagnostic purposes. The GA method was presented in this work for thermal-flow studies of steam turbines controlled from the central power system to obtain the stability of RESs. It should be remembered that the development of software using genetic algorithms to locate one-off degradations is necessary for a turbine that works sustainably with RESs. In this paper, against the background of the review, diagnostic procedures create an inverse model of a thermal power plant. Algorithms were used to detect fast global extremes through the convergence of simulated signatures with signs explaining degradation. In addition, statistical dependencies are used in the selection phase to accelerate fault detection. The created procedure allows obtaining a diagnosis in the form of a single degradation. This procedure turns out to be quite effective for the above example.