TS fuzzy prediction-based SVC compensation of wind farms flicker: A dual-UKF approach

Abstract

Fast fluctuations in wind farm power produce voltage flicker in the network. One way to mitigate the flicker is to place a static VAr compensator (SVC). Due to the operating delay of SVCs, it is essential to predict the wind farm reactive power. Here, a novel fuzzy nonlinear modeling approach is suggested and used in the one-step-ahead prediction of the power characteristics. The base of the developed fuzzy modeling is the Takagi-Sugeno fuzzy representation and a dual-unscented Kalman filter (D-UKF). In other words, a nonlinear TS fuzzy system is trained online via the D-UKF. The forecasted value is used as the SVC's reference signal. A large number of the actual data gathered from a wind farm are used for the performance evaluation. These data are collected in winter and summer for different climate situations. Using the actual data a current source with changing amplitude and phase which are updated every half-cycle is used to model the wind farm. Numerical results, including the flicker indices, confirm the improvement in the SVC's performance.

Modeling of Extremely Short-Time Power Variations of Wound Rotor Induction Machines Wind Farms for Flicker Studies

“…Hence, SVC by reducing the net reactive power that flows to the upstream network, can reduce the voltage flicker. The compensation of the reactive power is essential in wind and solar fields to mitigate their adverse impacts on the grid [34,35]. There are currently wind farms where SVC is installed and in operation [36].…”