Model predictive virtual power control of brushless doubly‐fed induction generator for fast and smooth grid synchronisation

Abstract

This study presents a model predictive virtual power control (MPVPC) method for brushless doubly-fed induction generator (BDFIG) during the grid synchronisation process. Predictive virtual power model is developed for the first time based on the defined virtual power and state-space equation of BDFIG. The control characteristics of virtual power are analysed to explain clearly how to realise grid synchronisation condition through the control of virtual power. The MPVPC controller is then designed to unify the control structure during the transition from the grid synchronisation mode to the grid connected mode, and only flux feedback needs to be changed between these two modes. The consistency between theoretical analysis and actual performance is confirmed by the results of both numerical simulation and experimental tests. As demonstrated, the proposed MPVPC controller can achieve fast and smooth grid synchronisation and excellent decoupled control of active power and reactive power. Nomenclature u sp , u r , u sc voltage vectors of power winding (PW), rotor, and control winding (CW) i sp , i r , i sc current vectors of PW, rotor, and CW ψ sp , ψ r , ψ sc flux vectors of PW, rotor, and CW ψ 1 , u g grid virtual flux and grid voltage vectors S p , S v PW-side and virtual complex power vectors P p , Q p PW-side active power and reactive power P v , Q v virtual active power and reactive power ω 1 , ω r , ω c electrical angular frequencies of grid, rotor, and CW θ p , θ 1 , θ g electrical angular positions of PW flux, grid virtual flux, and grid voltage p p , p c pole-pairs number of PW and CW R sp , R r , R sc resistances of PW, rotor, and CW L sp , L r , L sc self-inductances of PW, rotor, and CW L mp , L mc magnetising inductances of PW and CW

Modelling of brushless doubly fed reluctance machines based on reluctance network model

“…Also, Ψ st ∈ ℝ h�1 , Ψ rt ∈ ℝ l�1 , U st ∈ ℝ h�1 and U rt ∈ ℝ l�1 are the stator and rotor teeth fluxes vectors and the stator and rotor magnetic scalar potentials vectors, respectively. These vectors can be shown by Equations (8)(9)(10)(11).…”

Internal Model Control (IMC)-Based Active and Reactive Power Control of Brushless Double-Fed Induction Generator with Notch Filter

“…As part of the BDFIG control scheme, one of the primary objectives is to enhance the dynamic response, maintain constant power, and mitigate harmonics in the presence of loads [6]. In literature, researchers have attempted to control the BDFIG through different techniques that include model predictive virtual torque [7], power control [8], direct power controller (DPC) [9], and stator with indirect control quantities [10]. ese methods cannot offer an ideal control over the active and reactive powers with a suitable dynamic response.…”

Dynamic response enhancement of BDFIG using vector control scheme based internal model c ontrol

“…Several control strategies have been developed for controlling BDFIG behavior. The researchers have attempted to control the BDFIG through model predictive virtual power control in [9], direct power controller (DPC) in [10], predictive torque controller by matrix converter in [11], and indirect stator quantities control in [12], however, the mentioned control schemes lags the optimal regulation of speed and reactive power with suitable dynamic response. To overcome the stated issue, the authors have utilized one of the most prominent control schemes called vector control (VC) [13], [14].…”