Improving the efficiency of a wind turbine system using a fuzzy-pid controller

Baburajan, Silpa

doi:10.1109/icaset.2018.8376776

Cited by 13 publications

(12 citation statements)

References 4 publications

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“…In [55], a feed-forward FLC is integrated to accelerate the response of pitch control when there are increases in the wind speed change and then added to the output value of FLC-PID controller. The authors of [53] proposed a Fuzzy adaptive PID, where an FLC is used to calculate the gains for a PID control. In [54], an adaptive PI control is proposed, where the gains of the PI controller are set, but the generator power is used as the control variable.…”

Section: Results: Mppt Techniques Reviewmentioning

confidence: 99%

“…The authors of [53] proposed a Fuzzy adaptive PID, where an FLC is used to calculate the gains for a PID control. In [54], an adaptive PI control is proposed, where the gains of the PI controller are set, but the generator power is used as the control variable.…”

Section: Results: Mppt Techniques Reviewmentioning

confidence: 99%

“…Adaptive control algorithm calculates the gains of a PID controller in real time; the strategy to change the values of the gains is done with FLC[52][53][54].…”

mentioning

confidence: 99%

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Expert Control Systems for Maximum Power Point Tracking in a Wind Turbine with PMSG: State of the Art

et al. 2019

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Wind power is a renewable energy source that has been developed in recent years. Large turbines are increasingly seen. The advantage of generating electrical power in this way is that it can be connected to the grid, making it an economical and easily available source of energy. The fundamental problem of a wind turbine is the randomness in a wide range of wind speeds that determine the electrical energy generated, as well as abrupt changes in wind speed that make the system unstable and unsafe. A conventional control system based on a mathematical model is effective with moderate disturbances, but slow with very large oscillations such as those produced by turbulence. To solve this problem, expert control systems (ECS) are proposed, which are based on human experience and an adequate management of stored information of each of its variables, providing a way to determine solutions. This revision of recent years, mentions the expert systems developed to obtain the point of maximum power generation in a wind turbine with permanent magnet synchronous generator (PMSG) and, therefore, offers a control solution that adapts to the specifications of any wind turbine.

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Section: Results: Mppt Techniques Reviewmentioning

confidence: 99%

Section: Results: Mppt Techniques Reviewmentioning

confidence: 99%

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Expert Control Systems for Maximum Power Point Tracking in a Wind Turbine with PMSG: State of the Art

et al. 2019

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“…Pitch control methods in a wind turbine that mitigate changes in wind speed were found in the literature reviewed. In [13][14][15], a fuzzy logic controller (FLC) selects the previously calculated PID gains for the negative or positive error of the process variable, or if the error is very large. In [16], an artificial neural network determines the PID gain values.…”

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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“…A literature review was carried out and it was found that there are different authors who have worked to solve the problem of nonlinearity for pitch control in a wind turbine [20]. In [21][22][23] a fuzzy logic control (FLC) was combined with proportional-integral-derivative control (PID), FLC is the means to change previously calculated PID gains according to the process variable error, if the error is negative or positive or if the measured value greatly exceeds. In [24], a control method based on an artificial neural networks (ANN) adapter was presented in which the parameters of the PID neural network were automatically regulated.…”

Section: Introductionmentioning

confidence: 99%

Pitch Angle Optimization by Intelligent Adjusting the Gains of a PI Controller for Small Wind Turbines in Areas with Drastic Wind Speed Changes

et al. 2019

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The population growth demands a greater generation of energy, an alternative is the use of small wind turbines, however, obtaining maximum wind power becomes the main challenge when there are drastic changes in wind speed. The angle of the blades rotates around its longitudinal axis to control the effect of the wind on the rotation of the turbine, a proportional-integral controller (PI) for this angle achieves stability and precision in a stable state but is not functional with severe alterations in wind speed, a different response time is necessary in both cases. This article proposes a novel pitch angle controller based on auto-tuning of PI gains, for which it uses a teaching–learning based optimization (TLBO) algorithm. The wind speed and the value of the magnitude of the change are used by the algorithm to determine the appropriate PI gains at different wind speeds, so it can adapt to any sudden change in wind speed. The effectiveness of the proposed method is verified by experimental results for a 14 KW permanent magnet synchronous generator (PMSG) wind turbine located at the Universidad Autónoma de Querétaro (UAQ), Mexico.

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Improving the efficiency of a wind turbine system using a fuzzy-pid controller

Cited by 13 publications

References 4 publications

Expert Control Systems for Maximum Power Point Tracking in a Wind Turbine with PMSG: State of the Art

Expert Control Systems for Maximum Power Point Tracking in a Wind Turbine with PMSG: State of the Art

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

Pitch Angle Optimization by Intelligent Adjusting the Gains of a PI Controller for Small Wind Turbines in Areas with Drastic Wind Speed Changes

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