Abstract: A Takagi-Sugeno（T-S）fuzzy model identification approach based on modified fuzzy c-regression clustering aiming at the difficult problem of high-precision modeling of hydro-turbine generating units is proposed. Considering the importance of the error index between the sample output value and the output of the clustering hyperplane,the fuzzy c-regression clustering algorithm is improved. The algorithm assigns the reciprocal of the error value to the corresponding sample membership degree,and constructs a new weight matrix to update the clustering hyperplane,thereby accelerating the convergence of the clustering process towards the optimal clustering hyperplane. A new hyperplane-shaped membership function is proposed,which directly uses the hyperplane to identify the premise parameters. The recursive least squares algorithm with forgetting factor（FFRLS）is used to identify the consequent parameters of the model online. Taking three commonly used mathematical examples and a hydro-turbine generating unit of a hydropower station as the objects,the corresponding T-S fuzzy models are established and compared with other identification methods. The results indicate that the proposed fuzzy identification method has high identification accuracy,and the identified model has strong generalization ability.