Adaptive Control of Network Frequency by Doubly-Fed Induction Generators Using a Data-Driven Method

Nowadays, to involve wind turbines in controlling the network frequency, drop and inertia control methods are usually used for variable speed wind turbines. Adjusting the benefits of droop and inertia control loops is very effective on the performance of wind turbines, but due to the variability of wind speed and network conditions, adjusting the control coefficients that produce the best response in all conditions is impossible. In this paper, a new method for the comparative regulation of the gain of the droplet control loops and the inertia of the doubly-fed induction generator (DFIG) is presented. Also, due to the relief of problems and shortcomings of wind turbine and network modeling, the use of a data-driven method, which operates only on the input and output of the system, has been proposed. In the new idea presented for faster control and prevention of sharp frequency droop, new equations have been calculated to update the coefficients of the control loops using the second fault derivative, which is used in the comparative adjustment of the gain of the droop and inertia control loops. In the proposed control method, the next instantaneous output is first estimated using KVNN and then the coefficients of the frequency control loops are adjusted adaptively using the Hessian matrix. The simulation results for a DFIG wind farm show the proper performance of an adaptive method based on data-driven control in increasing the minimum frequency and improving the network frequency in a permanent and transient state when a fault occurs.