Intelligent Controller Based Wide-Area Control in Power System

A new strategy based on Takagi–Sugeno fuzzy controller is proposed in this paper for wide-area power system. In this strategy, the proposed fuzzy controller is optimized by a new hybrid metaheuristic algorithm and hyperplane clustering algorithm to partition the fuzzy space of the given input–output data. A global signal from the centralized controller is employed in wide area control (WAC) scheme to damp out the inter-area mode as well as local mode of oscillations. To demonstrate the capability of proposed strategy, three case studies have been used in this paper. The obtained results demonstrate the validity of proposed strategy through comparison with other techniques in this problem.

A Hierarchical Procedure for the Synthesis of ANFIS Networks

Adaptive neurofuzzy inference systems (ANFIS) represent an efficient technique for the solution of function approximation problems. When numerical samples are available in this regard, the synthesis of ANFIS networks can be carried out exploiting clustering algorithms. Starting from a hyperplane clustering synthesis in the joint input-output space, a computationally efficient optimization of ANFIS networks is proposed in this paper. It is based on a hierarchical constructive procedure, by which the number of rules is progressively increased and the optimal one is automatically determined on the basis of learning theory in order to maximize the generalization capability of the resulting ANFIS network. Extensive computer simulations prove the validity of the proposed algorithm and show a favorable comparison with other well-established techniques.