Adaptive filtering with robust controller for improvement of inertial response of wind turbine

Abstract

Summary It is necessary to study the impact of large‐scale wind turbines with virtual inertia control on power system stability considering the effects of frequency measurement. The phase‐locked loop (PLL) measures the system frequency. The operation of the PLL is deteriorated when the voltage contains sags, unbalance, high harmonics, and sudden drop frequency. Cascaded delayed signal cancelation (DSC) PLL technique can be used for frequency measurement under nonideal voltage due to its good harmonics filtering capability. But there are some oscillations and errors in the frequency estimation by the DSC‐PLL that led not to suitable frequency control in a doubly fed induction generator (DFIG). In this paper, an adaptive complex least mean square (ACLMS) algorithm is used for frequency estimation. The ACLMS algorithm is an adaptive filter, which can track frequency deviation, accurately and rapidly. In addition, this paper addresses robust frequency control in the DFIG. In a power system with wind turbines, load change, power fluctuation disturbance, and a dynamical perturbation influence the system frequency. In this condition, the conventional‐inertia controller is faced with challenges. H∞‐robust controller develops the frequency control loop. The estimated frequency using the ACLMS algorithm feeds the robust controller to improve the inertial response of the DFIG. This paper compares between H∞ and optimal conventional‐based inertia controller. The simulation validates that the ACLMS estimation with the H∞ inertia controller simultaneously provides frequency support to a low‐inertia tested power system, effectively. It improves frequency stability and resiliency under different disturbances and uncertainty.

A Journey From Improper Gaussian Signaling to Asymmetric Signaling

“…Interestingly, ACLMS algorithm renders lower steady-state mean-squared error than conventional CLMS in adaptive beamforming for multi-port antenna arrays [326], [330]. Furthermore, ACLMS usefulness in wind modelling and forecasting is unprecedented in renewable energy domain [327], [331]. Similarly, the approximate uncorrelating transform improved adaptive frequency estimations using ACLMS in three-phase power grid systems [328].…”

Digital twin-based subspace model predictive control for thermal power plant

“…The parameter perturbations always exist in actual power plants and affect the coordinated control performance. Generally speaking, common control methods such as active disturbance rejection control, 30,31 robust control, [32][33][34] output feedback integral control, 35 and MPC 13,36 have certain robustness and anti-interference ability so that the influence of parameter perturbations on outputs can be eliminated by adjusting the inputs. For the complex controlled object with large inertia and large time delays like power plants, although the system can maintain stable operation, the control performance will inevitably deteriorate.…”

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

“…Although using reduced-order EKF has eliminated the drawback of the Model Predictive Control method in perturbations, the inductance changes have not been discussed. Adaptive filtering and robust control have been introduced for frequency estimation and virtual inertia control [23].…”

“…e conventional inertia controller is not appropriate for DFIG in the presence of uncertainties. us, the H ∞ robust controller [23] develops the frequency control loop and the robust controller to improve the inertial response of the DFIG. An adaptive fuzzy fractional-order proportional-integral (FOPI) control system is proposed to improve the performance of a power grid-connected DFIGbased wind power conversion system [24].…”

“…e MH-DRPC strategy achieve sinusoidal forms of generated AC current with a constant frequency with minimal current total harmonic distortion and minimum output voltage ripple irrespective of the wind speed fluctuations and the robustness against parametric variations, but it has not been investigated about the measurement uncertainties. In contrast to [18][19][20][21][22][23][24][25][26] improving the performance of DFIG by using the H ∞ controller and other controllers, this paper intends to improve both the performance and the stability of the DFIG in the presence of both model and measurement uncertainties by proposing a new controller based on the combination of H ∞ controller method with the Kalman filter compared to the classical vector control. Table 1 shows the advantages and limitations of other control methods and the proposed method.…”