Hierarchical Pitch Control for Small Wind Turbines Based on Fuzzy Logic and Anticipated Wind Speed Measurement

Chavero-Navarrete, Ernesto; Trejo-Perea, Mario; Jáuregui-Correa, Juan Carlos; Carrillo-Serrano, Roberto V.; Ronquillo-Lomelí, Guillermo; Ríos-Moreno, José Gabriel

doi:10.3390/app10134592

Cited by 15 publications

(13 citation statements)

References 31 publications

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“…Fuzzy logic has been previously used to control the pitch angle of wind turbines. To mention some works, authors in [11] design a hierarchical fuzzy logic pitch controller to solve the nonlinear system effects produced by atypical winds. It is compared to a conventional PID pitch regulator.…”

Section: Related Workmentioning

confidence: 99%

“…It is composed of a fuzzy logic controller (FLC) and a module that obtains the effective wind speed using a deep learning system. The FLC receives the power reference, P ref , and the current output power, P out , to calculate the error (11) and generate the pitch reference, h ref . Besides, this intelligent controller also receives the effective wind speed, V DL , calculated by the deep learning module.…”

Section: Architecture Of the Hybrid Controllermentioning

confidence: 99%

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Deep learning and fuzzy logic to implement a hybrid wind turbine pitch control

Sierra-García

Santos

2021

Neural Comput & Applic

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This work focuses on the control of the pitch angle of wind turbines. This is not an easy task due to the nonlinearity, the complex dynamics, and the coupling between the variables of these renewable energy systems. This control is even harder for floating offshore wind turbines, as they are subjected to extreme weather conditions and the disturbances of the waves. To solve it, we propose a hybrid system that combines fuzzy logic and deep learning. Deep learning techniques are used to estimate the current wind and to forecast the future wind. Estimation and forecasting are combined to obtain the effective wind which feeds the fuzzy controller. Simulation results show how including the effective wind improves the performance of the intelligent controller for different disturbances. For low and medium wind speeds, an improvement of 21% is obtained respect to the PID controller, and 7% respect to the standard fuzzy controller. In addition, an intensive analysis has been carried out on the influence of the deep learning configuration parameters in the training of the hybrid control system. It is shown how increasing the number of hidden units improves the training. However, increasing the number of cells while keeping the total number of hidden units decelerates the training.

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Section: Related Workmentioning

confidence: 99%

Section: Architecture Of the Hybrid Controllermentioning

confidence: 99%

Deep learning and fuzzy logic to implement a hybrid wind turbine pitch control

Sierra-García

Santos

2021

Neural Comput & Applic

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“…In addition, the fuzzy control system could take into account different operating conditions, such as aspects related to the environment. In [9] a hierarchical fuzzy logic controller is designed to solve the nonlinear system effects produced by atypical winds. It proposes to install a wind speed measurement system at a calculated distance where the movement of the mechanical system of the pitch angle will anticipate the position of the setpoint angle, in order to minimize the effects of the wind gust on the rotation of the turbine.…”

Section: Related Workmentioning

confidence: 99%

Improving Wind Turbine Pitch Control by Effective Wind Neuro-Estimators

Sierra-García

Santos

2021

IEEE Access

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Data about wind are usually available from different databases, for different locations. In general, this information is the average of the wind speed over time. The wind reports are crucial for designing wind turbine controllers. But when working with floating offshore wind turbines (FOWT), two problems arise regarding the wind measurement. On the one hand, there are no buoys at deep sea, but near the coast where the wind is not so strong neither so stable; so the measurements do not fully correspond to reality. On the other hand, these floating devices are subjected to extreme environmental conditions (waves, currents, . . .) that produce disturbances and thus may distort wind measurements. To address this problem, this work presents a novel pitch neuro-control architecture based on neuro-estimators of the effective wind. The control system is composed of a proportional-integral-derivative (PID) controller, a lookup table, a neuro-estimator, and a virtual sensor. The neuro-estimator is used to estimate the effective wind in the FOWT and to forecast its future value. Both current and future wind signals are combined and power the controller. The virtual sensor also provides a measure of the effective wind based on other available signals related to the wind turbine, such as the pitch angle and the angular velocity of the generator. Neural networks are trained online to adapt to changes in the environment. Intensive simulations are carried out to validate the effectiveness of this neuro control approach. Controller performance is compared to a PID, obtaining better results. Indeed, an improvement of 16% for sinusoidal wind and an average improvement of 8% are observed.INDEX TERMS Floating offshore wind turbines (FOWTs), pitch control, neuro-estimator, neural network, virtual sensor, wind energy.

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“…However, in a variable-speed wind turbine, the optimal response changes as a function of the magnitude of the wind speed variation. This variability makes a PID controller unstable with drastic changes in the wind speed [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…In [20], a "flame-moth" optimization algorithm is proposed, the candidate solutions are moths, and the PID parameters are the position of the moths in a 3D search space. In [11], an optimization algorithm based on the teaching-learning model of a classroom is used to calculate the gains of a PI controller. These known methods have different disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Pitch Angle Optimization for Small Wind Turbines Based on a Hierarchical Fuzzy-PID Controller and Anticipated Wind Speed Measurement

et al. 2021

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Wind energy is an alternative to meet the growing energy demand and protect the environment; however, in places with limited wind resources, only the installation of small horizontal-axis wind turbines (SHAWTs) is profitable. At the height of these turbines, the wind is usually unstable with gusts and turbulence due to obstacles in its path such as buildings and trees. The pitch angle must be adaptable to guarantee nominal rotation speed, and it is commonly regulated with a proportional-integral-derivative (PID) feedback controller. This controller works well when the wind is stable, but not with drastic changes in wind speed. To correct this problem, this article introduces a PID controller with automatic adjustment of the gain values using a fuzzy logic controller (FLC). The PID gain adjustment allows an optimal response speed of the system for different wind conditions. The membership functions of the FLC are determined from a methodology that includes: The measurement of the wind speed at a calculated distance, a statistical analysis of the wind variability, and a dynamic analysis of the wind path. In this way, it is possible to anticipate the response of the actuator to the arrival of a gust of wind to the rotor. The algorithm is implemented in 14 kW SHAWTs where the difference in performance with a conventional controller is quantified. Satisfactory results were obtained, the electrical output increased by 7%, and the risk of rotor damage due to vibrations or mechanical fatigue was reduced by 20%.

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Hierarchical Pitch Control for Small Wind Turbines Based on Fuzzy Logic and Anticipated Wind Speed Measurement

Cited by 15 publications

References 31 publications

Deep learning and fuzzy logic to implement a hybrid wind turbine pitch control

Deep learning and fuzzy logic to implement a hybrid wind turbine pitch control

Improving Wind Turbine Pitch Control by Effective Wind Neuro-Estimators

Pitch Angle Optimization for Small Wind Turbines Based on a Hierarchical Fuzzy-PID Controller and Anticipated Wind Speed Measurement

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