Adaptive FOPI controller based on the fuzzy supervisory for wind power conversion system equipped by a doubly fed induction generator

Abstract

Summary Wind power systems have non‐linear dynamics and contain many uncertainties such as the parametric uncertainty and the unknown external disturbances. For these reasons, it is a crucial task to design the robust control systems to assure a robust response of wind systems during uncertainties without deteriorating the supplied power quality or stressing the static power converters. This paper designs an adaptive fractional‐order proportional‐integral (FOPI) control system for a wind power conversion system (WPCS) equipped by a doubly fed induction generator (DFIG) in the electric power grid (EPG) connected mode. The designed adaptive control system combines the robust and intelligent nature of the fuzzy supervisory system (FSS) and simple structure of FOPI controller, where the fuzzy rules are utilized to adjust the FOPI controller parameters based on the error and its rate, resulting in an adaptive fuzzy FOPI (AFFOPI) control system, which can keep the control action touch of the standard FOPI controller and, at the same time, provides the robustness during uncertainties without effect on the power quality supplied to the EPG. Pursuant to the vector control technology of the DFIG, the designed adaptive fuzzy FOPI controllers are applied in both the external loop of power control and in the internal loop of rotor current control, simultaneously. The feasibility of the presented adaptive FOPI control is validated through the numerical results obtained, under different running conditions, using the Matlab/Simulink software.

An SSM-PSO Based MPPT Scheme for Wind Driven DFIG System

“…, and  is tip speed ratio. The p C is calculated using (2), where C1 to C7 values are as represented in Table-II. From (2), i  is calculated in terms of  and  as (3).…”

“…Wind power is most promising because of its abundant accessibility and pollution less nature compared to the other available renewable sources. The usage of wind energy for the residential purpose, leads to energy independence [2].…”

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

“…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.…”

“…e main contribution of this paper is to control the output power of DFIG and keep it constant in the case of adding the worst possible uncertainty to the system, compared to the classical vector control. e rest of the paper is structured as follows: in Section 2, the DFIG model is introduced, and the design details of the robust H∞ controller method and Kalman filter are (i) e robustness during uncertainties [24] (ii) e robustness against parametric variations and good performances [25] (iii) e robustness against parametric variations and good performances [26] (i) Not checking measurement uncertainty (ii) Not checking parametric uncertainty [24] described in Section 3 and Section 4, respectively. en, the simulation results of the control method are illustrated in Section 5, which verifies the robustness and efficiency of the proposed controller with model and measurement uncertainties.…”