Power Control of a Grid-Connected Doubly Fed Induction Generator Using H∞ Control and Kalman Filter

Abstract

A new control method for a grid-connected doubly fed induction generator (DFIG) is proposed in this paper, which is robust against parametric uncertainty and measurement noise. In general, the DFIG controllers can be divided into two main groups: the rotor side converter (RSC) and the grid side converter (GSC) controllers. The parameters of a DFIG may deviate from their rated values due to the operating conditions. For this parametric uncertainty, a robust H∞ vector control (VC) is employed using the complex sensitivity approach. The design of the RSC controller has been carried out using the vector control strategy, and instead of proportional-integral (PI) controllers, a designed robust controller is used. One of the steps in vector control is the extraction of the measured currents to be used in the control equations. If the currents are polluted with noise, the system control will be impaired. Thus, using a Kalman filter is suggested to solve this problem. The effectiveness of the proposed method is then investigated using extensive simulations under various conditions. The obtained results confirm the efficient performance and robustness of the presented controller with model and measurement uncertainties.

Investigation of the Impact of SSSC-Based FLC on the Stability of Power Systems Connected to Wind Farms

“…As shown in Figure 2, the rotor winding of a DFIG is connected to a converter through slip rings, whereas its stator winding is directly connected to the network. Te electric power of the converter is about 20 to 30% of the total electric power of the generator [34].…”

Compensation characterization of the UPQC system under an improved nonlinear controller based on the MSTOGI-PLL device

“…Fuzzy control applied to the UPQC does not require an accurate mathematical model and is insensitive to internally caused disturbances and, thus, has good robustness, but it has the disadvantage of large errors in the regulation process, which makes it unsuitable for stand-alone use (Wu, 2022;Ye et al, 2022). The H∞ control UPQC has a small steady-state error, good robust performance, and is not susceptible to external interference, but the computational process is more complicated, and the response speed is slow (Li et al, 2015;Miquelez-Madariaga et al, 2022;Nezhad et al, 2022;Wang and Wang, 2022). Neural network control (NNC) overcomes the shortcomings of PI controllers that are susceptible to changes in external parameters and lead to a decrease in control performance, with strong learning ability, but the implementation is much more complex than that of the PI controller, and the dynamic response is slower (Patjoshi and Mahapatra, 2017;Liu et al, 2019).…”