A Heuristic T-S Fuzzy Model for the Pumped-Storage Generator-Motor Using Variable-Length Tree-Seed Algorithm-Based Competitive Agglomeration

With the fast development of artificial intelligence techniques, data-driven modeling approaches are becoming hotspots in both academic research and engineering practice. This paper proposes a novel data-driven T-S fuzzy model to precisely describe the complicated dynamic behaviors of pumped storage generator motor (PSGM). In premise fuzzy partition of the proposed T-S fuzzy model, a novel variable-length tree-seed algorithm based competitive agglomeration (VTSA-CA) algorithm is presented to determine the optimal number of clusters automatically and improve the fuzzy clustering performances. Besides, in order to promote modeling accuracy of PSGM, the input and output formats in the T-S fuzzy model are selected by an economical parameter controlled auto-regressive (CAR) model derived from a high-order transfer function of PSGM considering the distributed components in the water diversion system of the power plant. The effectiveness and superiority of the T-S fuzzy model for PSGM under different working conditions are validated by performing comparative studies with both practical data and the conventional mechanistic model.