Adaptation of Algorithms for Diagnostics of Steam Turbine Unit Equipment to Specific Conditions at Thermal Power Stations

At present, steam turbine manufacturers are interested in delivering diagnostic equipment systems for steam turbine units (STU) together with automatic control systems. These diagnostic systems should be adapted to a specific turbine size (or modification). The diagnostic system adaptation depends on the algorithm type used to detect defects. The following types of algorithms can be singled out: computational ones determining the defect using a computational model; expert ones based on probabilistic methods (using the summation of weighting coefficients of malfunction symptoms and the Bayes theorem); and digital ones based on the phenomenological relationships among process parameters, equipment state parameters, and other indicators obtained over the test period of equipment’s operation. The computational algorithms can be integrated into the turbine APCS and executed online (such as control system algorithms and steam admission system algorithms) or be used for handling postoperative tasks (such as diagnostics of auxiliary equipment or solving technical and economic problems). Expert algorithms are employed when development of a computational model involves great difficulties (e.g., in diagnosing a turbine thermal-expansion monitoring system or vibration-monitoring system). Digital algorithms are not related to the type of tasks, to specific equipment, or its features or operating modes. The degree of definiteness of a detected defect depends on the type of algorithm. The need to adapt diagnostic systems to other equipment involves not only the development of new diagnostic algorithms but also the formalization of the discourse. By this is meant the selection and justification of a standard state (prototype), i.e., a model state, to be used as a reference for development of diagnostic symptoms and a digital description of defect symptoms determined from the measurements and expert observations and opinions required for diagnosing.