Adaptive and predictive control strategies for wind turbine systems: a survey

Mahmoud, Magdi S.; Oyedeji, Mojeed O.

doi:10.1109/jas.2019.1911375

Cited by 37 publications

(15 citation statements)

References 114 publications

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“…The nonlinear MPC method using the exact physical models is still an unknown field. It not only needs to ensure the high fidelity of the model but also needs to shorten the calculation time as much as possible so that it is constrained within the sampling time of the system, which remains to be further explored [31]. The difficulty of the nonlinear MPC method lies in the complexity of its model and the calculation, and there are still very few relevant research studies.…”

Section: Nonlinear Mpcmentioning

confidence: 99%

“…Thus, it is essential to provide a summary of the application of the MPC technology to wind power control in wind farms. Application of MPC methods in wind control has been summarized in Mahmoud et al's work [31], and adaptive control has also been discussed in detail in that article. This paper provides an updated review of the relevant studies from 1997 to 2020, especially the studies conducted in the past five years, and focuses on discussing the commonly used MPC methods and the application scenarios of MPC in detail.…”

Section: Introductionmentioning

confidence: 99%

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A Review on the Application of the MPC Technology in Wind Power Control of Wind Farms

Li¹,

Hu²,

Yu³

et al. 2021

JEPT

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Due to the uncertainty and volatility of wind speed, its natural output power also exhibits strong volatility. In order to avoid the negative effects of the excessive fluctuation of the output power of wind turbines in the wind farm and the power grid, it is very important to accurately predict and reasonably plan and control the output power of the wind farm. Model predictive control (MPC) is a type of advanced control method that can deal with a multi-input multi-output nonlinear system. Compared to the traditional proportional integral derivative control, MPC is more suitable for the complex wind farm model and exhibits good control performance, and has been gradually applied to control the wind power in wind farms. In this article, we have summarized the application of the MPC technology in the prediction and control of wind power in a wind farm, analyze the application of the MPC technology, including MPC, multi-objective MPC, nonlinear MPC, and distributed MPC, in the wind farm power control with different optimization objectives. In addition, the optimization of the active and reactive power of wind farms has also been discussed in detail. Furthermore, some hot topics in the current research, such as the multi-objective optimization problem of coordinating the maximum power and reducing the fatigue load and power smoothing control problem, have been summarized. Finally, the existing problems of MPC applied to wind power systems have been discussed.

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Section: Nonlinear Mpcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Review on the Application of the MPC Technology in Wind Power Control of Wind Farms

Li¹,

Hu²,

Yu³

et al. 2021

JEPT

View full text Add to dashboard Cite

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“…The HAWT control design has gained significant importance during the last decades. Viable solutions available in the related literature may vary from linear PID [6], linear parameter varying control [7], adaptive nonlinear control [10], optimal control [11], evolutionary algorithms [12], robust control [13] and fuzzy logic systems [14]. However, these solutions fail to operate satisfactorily in the presence of faults.…”

Section: Introductionmentioning

confidence: 99%

Decoupling Adaptive Sliding Mode Observer Design for Wind Turbines Subject to Simultaneous Faults in Sensors and Actuators

Habibi

Howard

Simani

et al. 2021

IEEE/CAA J. Autom. Sinica

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This paper proposes an Adaptive Sliding Mode Observer (ASMO)-based approach for wind turbines subject to simultaneous faults in sensors and actuators. The proposed approach enables the simultaneous detection of actuator and sensor faults without the need for any redundant hardware components. Additionally, wind speed variations are considered as unknown disturbances, thus eliminating the need for accurate measurement or estimation. The proposed ASMO enables the accurate estimation and reconstruction of the descriptor states and disturbances. The proposed design implements the principle of separation to enable the use of the nominal controller during faulty conditions. Fault tolerance is achieved by implementing a signal correction scheme to recover the nominal behavior. The performance of the proposed approach is validated using a 4.8 MW wind turbine benchmark model subject to various faults. Monte-Carlo analysis is also carried out to further evaluate the reliability and robustness of the proposed approach in the presence of measurement errors. Simplicity, ease of implementation and the decoupling property are among the positive features of the proposed approach.

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“…The linear control has a drawback that while the control parameters were tuned for one operating point, the control performance would be degraded when the wind turbine worked away from the operating point. The gain scheduling technique was also investigated, 7,8 where adaptation of control parameters still relied on the measurement of wind speed. The optimal control with linear quadratic cost function was attempted in Duong et al 9 While it could provide steady output power, the method is linear and the implementation needs linearization.…”

Section: Introductionmentioning

confidence: 99%

Pitch angle control with fault diagnosis and tolerance for wind turbine generation systems

Shi

Wang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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To enhance the reliability of wind turbine generation systems that are generally located in the remote area and subjected to harsh environment, we design the pitch angle control for variable speed wind turbines with the function of fault diagnosis and fault tolerance. The main fault targeted in this research is the mechanical wear and possible break of the blade, pitch gear set or shaft, which cause shaft rotary friction change. The proposed method uses a disturbance observer to diagnose the fault. The estimated fault is used for component assessment and later maintenance. The fault-tolerant control is achieved using a full-order terminal sliding mode control combined with an adaptive neural network estimator. With the compensation of the adaptive estimator, the post-fault states can be driven onto the sliding surface and converge to a small area around the origin. The full-order terminal sliding mode control ensures the state convergence in finite time. The Lyapunov method is used to derive the control law, so that the closed-loop post-fault stability and the convergence of the adaptive estimator adaptation are both guaranteed. The computer simulations of the pitch angle control based on a 5-MW variable-speed variable-pitch angle wind turbine model are conducted with different types of fault simulated. A third-order nonlinear state space model with fault term is derived, and real physical parameters are applied in the simulations. The simulation results demonstrate the feasibility and effectiveness of the proposed scheme and the potential of real-world applications.

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Adaptive and predictive control strategies for wind turbine systems: a survey

Cited by 37 publications

References 114 publications

A Review on the Application of the MPC Technology in Wind Power Control of Wind Farms

A Review on the Application of the MPC Technology in Wind Power Control of Wind Farms

Decoupling Adaptive Sliding Mode Observer Design for Wind Turbines Subject to Simultaneous Faults in Sensors and Actuators

Pitch angle control with fault diagnosis and tolerance for wind turbine generation systems

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