Adaptive neuro-fuzzy algorithm to estimate effective wind speed and optimal rotor speed for variable-speed wind turbine

Asghar, Aamer Bilal; Liu, Xiaodong

doi:10.1016/j.neucom.2017.07.022

Cited by 78 publications

(42 citation statements)

References 48 publications

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“…Due to the nonlinearity and time-varying characteristics of the wind turbine, the conventional PID cannot meet the control requirements. The fuzzy algorithm transforms input values by fuzzifier and makes a judgment based on fuzzy rules, which can control the more complex systems [28][29][30][31][32]. In this paper, we use the fuzzy algorithm to adjust the parameters of the PID controller in the feedback control loop to improve the control performance of the controller.…”

Section: Feedforward Feedback Controller Based On Feedback Linearizatmentioning

confidence: 99%

Feedforward Feedback Pitch Control for Wind Turbine Based on Feedback Linearization with Sliding Mode and Fuzzy PID Algorithm

Ren

Zhang

Deng

et al. 2018

Mathematical Problems in Engineering

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After exceeding rated power, variable speed variable pitch wind turbines need to keep output powers at rated value by adopting pitch angles. With the typical nonlinear characteristics of the wind turbines, it is difficult to control accurately by conventional linear controller. Though feedback control can realize the stability of the system, it is effective only when deviations are produced. As such, feedforward control can be applied to compensate the time-delay produced by feedback control. Accordingly, we propose a compound control strategy that combines feedback controller with feedforward controller in this paper. In feedback loop, we adopt fuzzy algorithm to adjust the parameters of PID controller. Furthermore, to overcome large variation of input wind speed, variable universe theory is proposed to optimize fuzzy algorithm. In feedforward loop, we propose feedback linearization to address nonlinear problem. Furthermore, sliding mode algorithm is supplied to improve the robustness of feedback linearization. Therefore, feedforward loop can efficiently compensate time-delay deficiency of wind turbine systems. Simulation results show that the proposed controller can enhance the control accuracy and robustness of the system.

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Section: Feedforward Feedback Controller Based On Feedback Linearizatmentioning

confidence: 99%

Feedforward Feedback Pitch Control for Wind Turbine Based on Feedback Linearization with Sliding Mode and Fuzzy PID Algorithm

Ren

Zhang

Deng

et al. 2018

Mathematical Problems in Engineering

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“…Turbine blades can utilize the ratio of the turbine blade and turbine spinning at wind speed or tip speed ratio (λ) of the designed model as given in following Equation 2. Technically, the required wind speed limits for generators are capable of the producing ideal and effective electrical voltage at wind speeds of 5 -8 m/s [45], [51]- [58]. These speeds are also capable of rotating generators at speeds of 365 -480 rpm.…”

Section: Thermal and Wind Potentialsmentioning

confidence: 99%

Mitigation of the alternative energy for the wind farm center considering temperature and wind speed

2018

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Recently, electric usages are increasing every year by year in many sectors. In facts, fossil fuels have been fueled to produce electrical energy availability at many power plants which are very limited for the sustainable procurement. Developing and implementing renewable energy sources should be urgently promoted to reduce the dependence on fossil fuels that have been fueled to generate electricity for the long period throughout various power plant combinations. In expectation, the natural source of electrical energy which environmentally friendly and easy to obtain in nature is recommended to explore for the existing energy producers. The natural source of energy can be operated as an alternative power plant to reduce environmental effects and to decrease air contaminants. These works cover those opportunities. In these studies, the method used is a quantitative category with collected primary and secondary data for all evaluations and mitigations. In general, these works are also designed for identifying problems and looking for literature, data collection, processing stage, analysis phase, and final conclusion. The data used is defined in terms of temperature, air pressure, and wind speed. The collected data are supposed to the Purwoharjo City of Banyuwangi Regency, with 10 meters above ground level. Naturally, the wind speed is about 3.5 m/s to 4 m/s and the average temperature is 300˚ Kelvin. The potentially generated wind energy at a single point of coordinates is around 85.17 Wh.

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“…E VOLVING intelligent systems (EISs) [1], [2] are capable of effectively approximately modeling non-stationary problems in real time. In particular, they have been widely used in real world applications for streaming data processing [3], [4]. EISs self-organize and gradually self-develop their system structure and parameters through "one pass" learning process from data streams.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimized Autonomous Learning Fuzzy System

Shen

Angelov

2021

IEEE Trans. Cybern.

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The antecedent and consequent parts of a first-order evolving intelligent system (EIS) determine the validity of the learning results and overall system performance. Nonetheless, the state-of-the-art techniques mostly stress on the novelty from the system identification point of view but pay less attention to the optimality of the learned parameters. Using the recently introduced autonomous learning multiple model (ALMMo) system as the implementation basis, this paper introduces a particle swarmbased approach for EIS optimization. The proposed approach is able to simultaneously optimize the antecedent and consequent parameters of ALMMo and effectively enhance the system performance by iteratively searching for optimal solutions in the problem spaces. In addition, the proposed optimization approach does not adversely influence the "one pass" learning ability of ALMMo. Once the optimization process is complete, ALMMo can continue to learn from new data to incorporate unseen data patterns recursively without a full retraining. Experimental studies with a number of real-world benchmark problems validate the proposed concept and general principles. It is also verified that the proposed optimization approach can be applied to other types of EISs with similar operating mechanisms.

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Adaptive neuro-fuzzy algorithm to estimate effective wind speed and optimal rotor speed for variable-speed wind turbine

Cited by 78 publications

References 48 publications

Feedforward Feedback Pitch Control for Wind Turbine Based on Feedback Linearization with Sliding Mode and Fuzzy PID Algorithm

Feedforward Feedback Pitch Control for Wind Turbine Based on Feedback Linearization with Sliding Mode and Fuzzy PID Algorithm

Mitigation of the alternative energy for the wind farm center considering temperature and wind speed

Particle Swarm Optimized Autonomous Learning Fuzzy System

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