Intelligently Controlled Flywheel Storage for Enhanced Dynamic Performance

Abstract

This paper investigates the development and application of a nonlinear adaptive intelligent controller with superior disturbance-rejection capability for a doubly-fed-induction-machine driven flywheel energy storage system (FESS), to mitigate the intermittency in wind power injection, as well as enhance the transient stability of the connected multimachine power system thereby isolating the grid power from fluctuations. Intelligent supervisors for the rotor-side-converter of the FESS have been constructed using a fuzzy rule set. The consequent part in every rule includes a wavelet function for function learning. The novel normalized gradient descent algorithm with adaptive learning rate has been used to derive the update laws for unknown controller parameters. The control law has been derived by minimizing the predictive performance index. Convergence of the developed algorithm is guaranteed as proven via the Lyapunov stability method. Realistic wind speed profile has been used to test the efficacy of the developed control scheme. The sizing strategy has been devised for optimal sizing of FESS for its efficient utilization. Modified WSCC nine-bus test system has been used for the nonlinear time-domain simulations and novel indices have been used for evaluation of controller performance against its predecessors. $\boldsymbol{OP}{\text{5600}}$ Real-Time-Digital-Simulator has been used to demonstrate the real-time implementation of the suggested scheme.