Modeling of doubly fed induction generator based wind energy conversion system and speed controller

Abstract

In this paper, modeling of doubly fed induction generator (DFIG) based wind energy conversion system (WECS) and generator speed controller are presented. The System Identification Toolbox of MatLab is used to develop the linear model of the WECS by considering the wind speed as input and speed of the generator as output. Two models, namely Auto Regressive with eXogenous Input (ARX) and Auto-Regressive Moving Average with eXogenous Input (ARMAX), are estimated. We used the ARX221 model structure with the best fit of 84.31%, Final Prediction Error (FPE) of 0.0433 and Mean Square Error (MSE) of 0.0432. The Ziegler-Nichols (Z-N) method and the fuzzy logic technique are employed in the proportional integral derivative (PID) controller design to control the speed of the generator. The classical Z-N PID used for the responses of the system is observed to be insufficient for both uniform and variable inputs, hence, a better response has been obtained by applying the fuzzy logic-based PID controller. The present study proves that the fuzzy logic based control enhances the speed regulation of generator in the WECS by overcoming the effect of varying wind speed.

Analysis of the effect of parametric uncertainty on dynamic performances of doubly fed induction generator-based wind energy conversion system

“…Only a portion of available power in the wind ( P w ) can be converted to mechanical by wind turbines. Accordingly, the wind turbine rotor output power ( P t ) and torque ( T t ) are described as (Haile et al, 2021; Tuka and Endale, 2023):…”

“…In the modeling of the wind turbine train, the main elements to be considered can be the rotor dynamics, low and high-speed shaft with gearbox, and the machine itself. The DFIG with drive train in the WECS is given by mathematical models in Haile et al (2021). In this regard, a mathematical model under the consideration of a single mass model of the DFIG in a wind turbine system is given:…”

“…The authors Benzaouia et al (2020), Jalal and Ganjefar (2019), Li et al (2019), Rezaei et al (2019) carried their work by modeling a conventional Permanent Magnetic Synchronous Generator (PMSG)-based wind turbine system to estimate and control the generator rotor speed and its output power while Srikanth and Thirumalaivasan (2022) presents a grid tied-DFIG model for the same purpose. Similarly, the author of Haile et al (2021), Tuka (2023), Tuka and Endale (2023)estimated the model of the same machine by taking the relationship between generator rotor speed with the speed of the wind under dynamic conditions. On the other hand, a mathematical model of a DFIG-based wind turbine using Model Predictive Control is depicted in detail by Alfeu (2022).…”

Experimental Validation of Predictive Current Control for DFIG: FPGA Implementation

“…For this reason, it is necessary to convert the system's continuous-time (CT) representation, presented in Equation (11), to the DT representation presented in Equation ( 12). This discretization is accomplished through the application of the transformation shown in Set Equation (13) [30][31][32][33] .…”

“…According to [12], an adaptive controller based on fuzzy logic and PI is proposed for a DFIG-based wind energy system; however, these controllers frequently introduce chattering phenomena and require previous knowledge of wind speed. The authors in [13] presented the Ziegler-Nichols (ZN) method employed in a proportional integral derivative (PID) controller design to control the speed of the DFIG; the system was observed to be insufficient for both uniform and variable inputs.…”