Abstract
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.
Highlights
With the ever-increasing interconnection of intermittent renewable energy in modern power systems, the pumped-storage power plant (PSPP) plays a significant role in maintaining the balance of power supply and demand due to its flexible operations in both generation and pumping directions [1]
Considering the value ranges of the rotation speed and guide vane opening in all aforementioned working conditions, the parameters setting of the proposed TSA-competitive agglomeration (CA) based T-S fuzzy model is given below: The search space for clustering centers vik is set as vik ∈ [−1, 1], for k = 1, 2, · · ·, n a and vik ∈ [0, 1] for k = n a + 1, n a + 2, · · ·, n a + nb according to the value ranges of rotor speed and guide vane opening, respectively
As the determination of the optimal cluster number c which determines the number of fuzzy rules is one of the difficulty in T-S fuzzy modeling, the variable-length tree-seed algorithm based competitive agglomeration (VTSA-CA) algorithm is introduced to specify the optimal number of clusters and the best fuzzy partition of the system
Summary
With the ever-increasing interconnection of intermittent renewable energy in modern power systems, the pumped-storage power plant (PSPP) plays a significant role in maintaining the balance of power supply and demand due to its flexible operations in both generation and pumping directions [1]. Differential equation and transfer function models is developed to mimic the dynamic behaviors of water hammer effect in diversion tunnels and penstocks [10,11,12,13,14] These methods would more or less suffer from the deficiencies of heavy online computation burden or the modeling. Considering advantages of the automatic cluster number determination in CA algorithm and the preponderant global optimization capability in swarm intelligent optimizers, a modified CA algorithm coupled with a novel variable-length tree-seed algorithm (VTSA) which we called variable-length tree-seed algorithm based competitive agglomeration (VTSA-CA) algorithm is presented in this paper. The proposed VTSA-CA clustering method is used to the antecedent structure identification of T-S fuzzy modeling to take the place of traditional clustering approaches or state-or-the-art heuristic optimization algorithms.
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