Current status and future prospects of continuously variable speed wind turbines: A systematic review

Yin, Xiuxing; Zhang, Wencan; Zhao, Xiaowei

doi:10.1016/j.ymssp.2018.05.063

Cited by 29 publications

(19 citation statements)

References 16 publications

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“…. Considering (1) and (21), it is obvious that the rotor speed is controlled by regulating the pitch angle and the consequent aerodynamic torque. In this paper, it is assumed that at any operational point of the wind turbine,…”

Section: Desired Operational Mode and Control Objectivesmentioning

confidence: 99%

“…Also, it should be noted that as the wind speed is not accurately measurable, so, ) in (23) is an unknown variable. Substituting (23) into (21) leads to,…”

Section: Desired Operational Mode and Control Objectivesmentioning

confidence: 99%

“…This leads to the unknown control direction problem. Therefore, considering uncertain wind speed variation in the pitch angle control design of wind turbines is a significant challenge [2,21]. In [22] a nonstandard extended Kalman filter is developed to estimate the wind speed for maximum power extraction of variable speed wind turbines.…”

Section: Introductionmentioning

confidence: 99%

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Fault-Tolerant Neuro Adaptive Constrained Control of Wind Turbines for Power Regulation with Uncertain Wind Speed Variation

Habibi¹,

Rahimi²,

Howard³

et al. 2019

Preprint

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This paper presents a novel adaptive fault-tolerant neural-based control design for wind turbines with unknown dynamic and unknown wind speed. By utilizing the barrier Lyapunov function in the analysis of the Lyapunov direct method, the constrained behavior of the system is provided in which the rotor speed, its variation and generated power remain in the desired bounds. In addition, input saturation is also considered in terms of smooth pitch actuator bounding. Furthermore, by utilizing a Nussbaum-type function in designing the control algorithm, the unpredictable wind speed variation is captured without requiring accurate wind speed measurement, observation or estimation. Moreover, with the proposed adaptive analytic algorithms, together with the use of radial basis function neural networks, a robust adaptive and fault-tolerant control scheme is developed without the need for precise information about the wind turbine model nor the pitch actuator faults. Additionally, the computational cost of the resultant control law is reduced by utilizing a dynamic surface control technique. The effectiveness of the developed design is verified using theoretical analysis tools and illustrated by numerical simulations on a high-fidelity wind turbine benchmark model with different fault scenarios. Comparison of the achieved results to the ones that can be obtained via an available industrial controller shows the advantages of the proposed scheme.

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Section: Desired Operational Mode and Control Objectivesmentioning

confidence: 99%

“…Also, it should be noted that as the wind speed is not accurately measurable, so, ) in (23) is an unknown variable. Substituting (23) into (21) leads to,…”

Section: Desired Operational Mode and Control Objectivesmentioning

confidence: 99%

See 1 more Smart Citation

Fault-Tolerant Neuro Adaptive Constrained Control of Wind Turbines for Power Regulation with Uncertain Wind Speed Variation

Habibi¹,

Rahimi²,

Howard³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Wind power, which is one of the most promising renewable resources, proved to be an ideal alternative . However, it is found that the randomness of wind speed has a serious impact on the stability of power system . At present, there are two ways to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

A prediction approach using ensemble empirical mode decomposition‐permutation entropy and regularized extreme learning machine for short‐term wind speed

Tian

Wang

2019

Wind Energy

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Accurate prediction of short-term wind speed is of great significance to the operation and maintenance of wind farms, the optimal scheduling of turbines, and the safe and stable operation of power grids. A prediction approach for short-term wind speed using ensemble empirical mode decomposition-permutation entropy and regularized extreme learning machine is proposed. Firstly, wind speed time series is decomposed into several components with different frequency by ensemble empirical mode decomposition, which can reduce the non-stationarity of the original time series. The permutation entropy value for each component is used to analyze its complexity. The components can be recombined to obtain a set of new subsequences. Then, different prediction models based on regularized extreme learning machine are used to predict each subsequence. Fivefold cross validation is used to improve the reliability of the regularized extreme learning machine model. Finally, the predicted value of each subsequence is superimposed to obtain the final predictive result. Ten minutes, 30 minutes, and 1 hour short-term wind speed data from wind farms in Liaoning Province, China, are used for conducting experiments. The experimental results indicate that the values of the root mean square error of the developed prediction approach utilizing 10 minutes, 30 minutes, and 1 hour interval data are 0.5629, 0.4473, and 0.5697; mean absolute error are 0.4427, 3.0701, and 0.4897; mean absolute percentile error are 4.1456%, 16.8166%, and 6.8166%; relative root mean square are 0.0505, 0.2997, and 0.2609; square sum error are 55.5263, 59.6347, and 64.9154; and the Theil inequality coefficient are 0.0235, 0.0808, and 0.0625, which are much lower than those of the comparison methods. The values of the R square of the developed prediction approach utilizing 10 minutes, 30 minutes, and 1 hour interval data are 0.9363, 0.9161, and 0.9472, and the index of agreement are 0.9994, 0.9925, and 0.9894, whichare higher than those of the comparison methods. The Pearson's test results show that the association strength between the actual value and the predicted values of the proposed approach is stronger. Also, the proposed prediction approach in this paper has higher reliability under the same confidence level. The effectiveness of the proposed prediction approach for short-term wind speed is verified.

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“…But problems like the more mass and complexity caused by traditional mechanical transmission system hinder the development of the offshore wind turbine [2]. Recently, the investigation of hydraulic wind turbine have been conducted deeply [3][4][5] to overcome this problem. In [6], authors combined a closed oil circuit and an open seawater circuit as a energy transmission system.…”

Section: Introductionmentioning

confidence: 99%

Maximum Power Extraction and Pitch Angle Control for Offshore Wind Turbine with Open-Loop Hydraulic Transmission

Fan¹,

Mu²

2019

Proceedings of the 2019 International Conference on Precision Machining, Non-Traditional Machining and Intelligent Manufacturin

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This paper aims to extract optimal power from a hydraulic floating wind turbine using torque control and pitch control. When wind speed is below rated value, a nonlinear fuzzy controller is designed to capture maximum wind energy through regulating the displacement of hydraulic pump fixed in nacelle. When wind speed is above rated value, to achieve the pitch angle control and guarantee that the generation system safely works, an adaptive fuzzy controller is built and the stability of the control system has been analyzed through Lyapunov criterion. In order to validate the feasibility of the proposed controllers, the system has been simulated in Matlab/simulink under random wind speeds. The results show that as the wind speed is below the rated value the power coefficient has been kept to the optimal value. Furthermore, the maximum output power has been held near to rated value as the wind speed is above the rated value.

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Current status and future prospects of continuously variable speed wind turbines: A systematic review

Cited by 29 publications

References 16 publications

Fault-Tolerant Neuro Adaptive Constrained Control of Wind Turbines for Power Regulation with Uncertain Wind Speed Variation

Fault-Tolerant Neuro Adaptive Constrained Control of Wind Turbines for Power Regulation with Uncertain Wind Speed Variation

A prediction approach using ensemble empirical mode decomposition‐permutation entropy and regularized extreme learning machine for short‐term wind speed

Maximum Power Extraction and Pitch Angle Control for Offshore Wind Turbine with Open-Loop Hydraulic Transmission

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