A Control Strategy of Leveling Load Power Fluctuation by Fuzzy-Neural Network with Auto-acquiring of Membership Functions

Abstract

The fuzzy control strategy of superconducting magnetic energy storages (SMESes) was proposed for leveling fluctuating active power and compensating reactive power. The control results depend on the values of scaling factors and membership functions in fuzzy reasoning. Therefore, it is desired to obtain better control results that the scaling factors and membership functions are successively adjusted according to the load power fluctuation.
In this paper, a new control strategy of leveling load power fluctuation by fuzzy-neural network with auto-acquiring of membership functions is proposed. Neural networks are used for an acquiring method of membership functions in fuzzy reasoning rules and auto-tuning of scaling factors and grade of fuzzy reasoning.