Fault-Tolerant Neuro Adaptive Constrained Control of Wind Turbines for Power Regulation with Uncertain Wind Speed Variation
| Autor: | Silvio Simani, Hamed Habibi, Ian Howard, Hamed N. Rahimi |
|---|---|
| Přispěvatelé: | UCL - SST/IMMC/MEED - Mechatronic, Electrical Energy, and Dynamics Systems, Curtin Universtity - School of Civil and Mechanical Engineering, University of Ferrara - Department of Engineering |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Fault-tolerant control
Power regulation 0209 industrial biotechnology Control and Optimization Computer science PE7_7 PE8_6 robustness evaluation 020209 energy barrier Lyapunov function Control (management) Energy Engineering and Power Technology PID controller 02 engineering and technology power regulation Fault (power engineering) Turbine Wind speed Adaptive constrained control 020901 industrial engineering & automation Economica Control theory control_systems_engineering 0202 electrical engineering electronic engineering information engineering PE7_3 Barrier lyapunov function Electrical and Electronic Engineering PE7_4 Engineering (miscellaneous) PE7_1 Wind power Renewable Energy Sustainability and the Environment business.industry Nussbaum-type function Ambientale Fault tolerance fault-tolerant control Variation (linguistics) pitch actuator Adaptive constrained control barrier Lyapunov function fault-tolerant control Nussbaum-type function pitch actuator power regulation robustness evaluation Benchmark (computing) Environmental science Actuator business Energy (miscellaneous) |
| Zdroj: | Energies Volume 12 Issue 24 Energies, Vol. 12, no.24, p. 4712 (2019) |
| DOI: | 10.0245/v1 |
| Popis: | This paper presents a novel adaptive fault-tolerant neural-based control design for wind turbines with an 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. |
| Databáze: | OpenAIRE |
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