Resumen
The task on estimating the technical condition of a hydrogenerator under conditions of fuzzy information has been resolved. To this end, a series of models have been constructed for the integrated estimation of the technical condition of a hydrogenerator based on data about the states of its local nodes. The technical states of local nodes are determined based on the earlier devised fuzzy models of the Mamdani type and represent the fuzzy values, which was taken into consideration in the model for estimating technical condition of a hydrogenerator.The fuzzy methods by Mamdani, Sugeno, Zadeh, as well as the simplified fuzzy inference, were used to build the models. The fuzzy model by Mamdani has a qualitative base of rules only, which simplifies its construction by an expert. The models based on the fuzzy algorithm by Sugeno imply a rule base with weight coefficients, determined by the Saati method. The simplified method and the method by Zadeh require minimal expert participation when constructing a fuzzy model. Examples of estimating the technical condition of a hydrogenerator have been considered based on five devised fuzzy models; the sensitivity of models to the quality and reliability of input information has been tested.It has been determined that the most reliable result from estimating the state of a hydrogenerator with an error of 1.5?2 % is produced by models built according to Zadeh method and the simplified fuzzy inference, since they have the least dependence on the uncertainty of input data on the states of local nodes, which themselves were obtained based on fuzzy models. High accuracy of these models and low dependence on the quality of incoming information are explained by the minimal participation of an expert during its configuration. The fuzzy models built using the algorithms by Mamdani and Sugeno yield a greater error of 3?4 %. Oure findings could be used to assess the remaining or spent resource of hydrogenerators, the probability of their failure over a time interval, and to execute the risk-oriented control over an electricity energy system and its subsystems