Analysis and Control of Fault Ride-Through Capability Improvement for Wind Energy Conversion System Using Linear Active Disturbance Rejection Control With Correction Link

Ma, Youjie; Long, Taotao; Zhou, Xuesong; Shi, Xueqi

doi:10.1109/access.2020.2987103

Cited by 22 publications

(12 citation statements)

References 34 publications

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“…In this study, a model control based on ANFIS is adopted for the controller. The proposed WECS factors and parameters are listed in the Appendix of this paper [8,9]. The state dynamic formulation of the WECS are at normal load point is described as follows,…”

Section: Wind Energy Conversion Systemmentioning

confidence: 99%

“…Wind energy conversion systems (WECS) are a pollution-free and effective source to be integrated into both distribution and transmission systems [5], [6]. An essential requirement with the increasing trend of WECS in the unified power systems is to abstract the maximum accessible power from the wind power considering the fluctuations of the wind speed and direction [7], [8]. Because of such intermittent nature of unexpected wind speed, determining the optimum speed of WECS so as to attain the maximum wind power is obligatory at any wind speed.…”

Section: Introductionmentioning

confidence: 99%

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Robust Design of ANFIS-Based Blade Pitch Controller for Wind Energy Conversion Systems Against Wind Speed Fluctuations

et al. 2021

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Wind speed fluctuations and load demand variations represent the big challenges against wind energy conversion systems (WECS). Besides, the inefficient measuring devices and the environmental impacts (e.g. temperature, humidity, and noise signals) affect the system equipment, leading to increased system uncertainty issues. In addition, the time delay due to the communication channels can make a gap between the transmitted control signal and the WECS that causes instability for the WECS operation. To tackle these issues, this paper proposes an adaptive neuro-fuzzy inference system (ANFIS) as an effective control technique for blade pitch control of the WECS instead of the conventional controllers. However, the ANFIS requires a suitable dataset for training and testing to adjust its membership functions in order to provide effective performance. In this regard, this paper also suggests an effective strategy to prepare a sufficient dataset for training and testing of the ANFIS controller. Specifically, a new optimization algorithm named the mayfly optimization algorithm (MOA) is developed to find the optimal parameters of the proportional integral derivative (PID) controller to find the optimal dataset for training and testing of the ANFIS controller. To demonstrate the advantages of the proposed technique, it is compared with different three algorithms in the literature. Another contribution is that a new time-domain named figure of demerit is established to confirm the minimization of settling time and the maximum overshoot in a simultaneous manner. A lot of test scenarios are performed to confirm the effectiveness and robustness of the proposed ANFIS based technique. The robustness of the proposed method is verified based on the frequency domain conditions that are driven from Hermite-Biehler theorem. The results emphases that the proposed controller provides superior performance against the wind speed fluctuations, load demand variations, system parameters uncertainties, and the time delay of the communication channels.

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Section: Wind Energy Conversion Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Design of ANFIS-Based Blade Pitch Controller for Wind Energy Conversion Systems Against Wind Speed Fluctuations

et al. 2021

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“…The method used for the generator design is a combined Analytic-Finite element method (Ali et al, 2020a, 2020b, 2021; Aly et al, 2017; Beltran et al, 2008; Bhattacharyya et al, 2020; Cheng et al, 2018; Cross et al, 2015; Das et al, 2020; Ebrahim et al, 2012; Ebrahim et al, 2012; Ebrahim et al, 2018; Elsisi et al, 2021; Elsisi, 2020; Geng and Yang, 2014; Hussain and Mishra 2020; Jang, 1993; Kennedy and Eberhart, 1995; Khosravi et al, 2020; Li et al, 2021; Liang et al, 2021; Liu et al, 2020; Ma et al, 2020; Man et al, 1996; Nikiforow et al, 2018; Ouyang et al, 2019; Pan et al, 2020; Przybylek and Gielniak, 2019; Soued et al, 2017; Sugeno and Tanaka, 1991; Yang, 2014; Zervoudakis and Tsafarakis, 2020).…”

Section: Introductionmentioning

confidence: 99%

Conversion of AC wind energy system model to DC model for integration to optimization software with large scales

Tounsi

2021

Wind Engineering

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The work presented in this paper deals with an AC model of wind turbine system conversion to a DC model with reduced simulation time, for possible integration to optimization software with larges scales permitting a multi-objective optimization, such as the constrained optimization conjointly of the cost and power losses of the wind turbine energy system. The DC model is based on average calculation of the DC voltage recharging the battery energy accumulator used for recovering the converted wind energy and the electromagnetic torque. Indeed, classical model of wind turbine using generally electric generator associated to a PD3 rectifier to convert the alternative energy on DC energy recoverable on battery energy accumulator with need a large simulation time and thereafter it is non integrable to optimization software for multi-objective optimization problem resolution. The two models are implemented under Matlab-Simulink simulation environment. Simulation results valid entirely the wind turbine system DC model. Finally, as perspective it is interesting to use a booster chopper as an interface between the rectifier and the battery to optimize the recovered energy. An average model of the booster chopper can be integrated into the DC model for performance improvement of the conversion chain.

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“…The active disturbance rejection controller was synthesized for the WECS in papers [22] and [23], but these two papers only consider the existence of the disturbance and did not concern the influence of the uncertainties. With above analyses, it is seen that the previous papers either deal with the uncertainties [9], [20], [21] or take into consideration of the disturbance [22], [23]. To the best of our knowledge, up-to-now, there is not any paper considering the WECS system with the existence of both uncertainties and disturbance simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Robust MPPT Observer-Based Control System for Wind Energy Conversion System With Uncertainties and Disturbance

Ngo

et al. 2021

IEEE Access

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The problem of tracking the maximum power point for the wind energy conversion system (WECS) is taken into consideration in this paper. The WECSs in this article is simultaneously affected by the uncertainties and the arbitrary disturbance that cause the WECSs to be much more challenging to control. A new method to synthesize a polynomial disturbance observer for estimating the aerodynamic torque, wind speed, and electromagnetic torque without using sensors is proposed in this paper. Unlike the previous methods, in this work, both the uncertainties and the disturbance are estimated, then estimations of the uncertainties and disturbance are transmitted to the Linear Quadratic Regular (LQR) controller for eliminating the influences of the uncertainties and disturbance; and tracking the optimal power point of WECS. It should be noted that the uncertainties in this work are time-varying and both uncertainties and disturbance do not need to satisfy the bounded constraints. The wind speed and aerodynamic torque are arbitrary and unnecessary to fulfil the low-varying constraint or r th time derivative bound. On the basis of Lyapunov methodology and sum-of-square technique (SOS), the main theorems are derived to design the polynomial disturbance observer. Finally, the simulation results are provided to demonstrate the effectiveness and merit of the proposed method.

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Analysis and Control of Fault Ride-Through Capability Improvement for Wind Energy Conversion System Using Linear Active Disturbance Rejection Control With Correction Link

Cited by 22 publications

References 34 publications

Robust Design of ANFIS-Based Blade Pitch Controller for Wind Energy Conversion Systems Against Wind Speed Fluctuations

Robust Design of ANFIS-Based Blade Pitch Controller for Wind Energy Conversion Systems Against Wind Speed Fluctuations

Conversion of AC wind energy system model to DC model for integration to optimization software with large scales

Robust MPPT Observer-Based Control System for Wind Energy Conversion System With Uncertainties and Disturbance

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