Individual pitch control of wind turbine based on RBF neural network

Liu, Zhongpeng; Huo, Feng; Xiao, Shuowen; Zhu, Shilong; Ji, Guorui; Dai, Haitao

doi:10.1109/chicc.2016.7554259

Cited by 7 publications

(2 citation statements)

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“…This configuration is improved in [19] by using NPI control and in [32] by using FONPI control. In [124], the PI controllers of the IPC are combined with a radial basis function neural network such that the PI controllers are better adapted to the nonlinearities. The results show an important improvement in the load reduction.…”

Section: Control Laws Applied To the Ipcmentioning

confidence: 99%

Pitch Control of Three Bladed Large Wind Energy Converters—A Review

Gambier

2021

Energies

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Modern multi-megawatt wind turbines are currently designed as pitch-regulated machines, i.e., machines that use the rotation of the blades (pitching) in order to adjust the aerodynamic torque, such that the power is maintained constantly throughout a wide range of wind speeds when they exceed the design value (rated wind speed). Thus, pitch control is essential for optimal performance. However, the pitching activity is not for free. It introduces vibrations to the tower and blades and generates fatigue loads. Hence, pitch control requires a compromise between wind turbine performance and safety. In the past two decades, many approaches have been proposed to achieve different objectives and to overcome the problems of a wind energy converter using pitch control. The present work summarizes control strategies for problem of wind turbines, which are solved by using different approaches of pitch control. The emphasis is placed on the bibliographic information, but the merits and demerits of the approaches are also included in the presentation of the topics. Finally, very large wind turbines have to simultaneously satisfy several control objectives. Thus, approaches like collective and individual pitch control, tower and blade damping control, and pitch actuator control must coexist in an integrated control system.

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Section: Control Laws Applied To the Ipcmentioning

confidence: 99%

Pitch Control of Three Bladed Large Wind Energy Converters—A Review

Gambier

2021

Energies

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“…Liu et al [77] developed another individual pitch controller. They presented a RBFNN with online training.…”

Section: B Artificial Neural Networkmentioning

confidence: 99%

Expert Control Systems Implemented in a Pitch Control of Wind Turbine: A Review

et al. 2019

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Wind energy is the strongest renewable energy source developed in recent decades. Being systems that are directly connected to the grid of the electrical system, it is essential to use the maximum available power of the wind and obtain the maximum electrical power converted from the turbine. In this paper, the fundamental problem of the wind turbine is how to obtain at all times the maximum output power of the turbine in a wide range of wind speed. The randomness of the wind adds an intrinsic difficulty to be able to plan the available wind energy in advance. To solve this problem, it is not necessary to know the dynamic operation of the system; we must anticipate the control response to each one of the different probable scenarios. An expert control system can be used based on human knowledge and experience, which, through proper management of its variables and adequate control of criteria to manipulate stored data, provides a way to determine solutions. In other words, it is a model of the experience of professionals in this field. The more variables in the system are considered, the more complete the model will be, and the more information will be available for decision-making, with a more efficient system and higher results in power generation as a response. For this reason, the objective of this paper is to present expert systems developed in recent years and, thus, offer a control solution that approximates the conditions of different wind turbines.INDEX TERMS Artificial neural network, fuzzy logic, genetic algorithms, wind power generation, control systems.

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