Effectiveness of Nature-Inspired Algorithms using ANFIS for Blade Design Optimization and Wind Turbine Efficiency

Sarkar, Md Rasel; Julai, Sabariah; Chong, Wen Tong; Toha, Siti Fauziah

doi:10.3390/sym11040456

Cited by 14 publications

(7 citation statements)

References 59 publications

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“…The ANFIS model with the prediction value closest to the actual output is selected as the optimum model [33]. The output variables are predicted from the optimum ANFIS model by importing the input variables into the rule viewer [34]. Figure 4 shows the structure of formulated ANFIS model to predict the performance of the thermoelectric generator system for waste heat recovery.…”

Section: Adaptive Neuro-fuzzy Interface System Modelling (Anfis)mentioning

confidence: 99%

Artificial Neural Network and Adaptive Neuro-Fuzzy Interface System Modelling to Predict Thermal Performances of Thermoelectric Generator for Waste Heat Recovery

et al. 2020

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The present study elaborates the suitability of the artificial neural network (ANN) and adaptive neuro-fuzzy interface system (ANFIS) to predict the thermal performances of the thermoelectric generator system for waste heat recovery. Six ANN models and seven ANFIS models are formulated by considering hot gas temperatures and voltage load conditions as the inputs to predict current, power, and thermal efficiency of the thermoelectric generator system for waste heat recovery. The ANN model with the back-propagation algorithm, the Levenberg–Marquardt variant, Tan-Sigmoidal transfer function and 25 number of hidden neurons is found to be an optimum model to accurately predict current, power and thermal efficiency. For current, power and thermal efficiency, the ANFIS model with pi-5 or gauss-5-membership function is recommended as the optimum model when the prediction accuracy is important while the ANFIS model with gbell-3-membership function is suggested as the optimum model when the prediction cost plays a crucial role along with the prediction accuracy. The proposed optimal ANN and ANFIS models present higher prediction accuracy than the coupled numerical approach.

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Section: Adaptive Neuro-fuzzy Interface System Modelling (Anfis)mentioning

confidence: 99%

Artificial Neural Network and Adaptive Neuro-Fuzzy Interface System Modelling to Predict Thermal Performances of Thermoelectric Generator for Waste Heat Recovery

et al. 2020

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“…Dalibor et al (2014) designed an ANFIS controller to adjust the continuous variable transmission ratio in order to maintain the maximum output power of a wind turbine. Sarkar et al (2019) implemented an ANFIS approach to predict the power coefficient of wind turbine blades. Their results demonstrated that the predicted values were very close to the objective values.…”

Section: Introductionmentioning

confidence: 99%

Smoothing the output power of a wind energy conversion system using a hybrid nonlinear pitch angle controller

Fan

2021

Energy Exploration & Exploitation

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Wind energy has been developed and is widely used as a clean and renewable form of energy. Among the existing variety of wind turbines, variable-speed variable-pitch wind turbines have become popular owing to their variable output power capability. In this study, a hybrid control strategy is proposed to implement pitch angle control. A new nonlinear hybrid control approach based on the Adaptive Neuro-Fuzzy Inference System and fuzzy logic control is proposed to regulate the pitch angle and maintain the captured mechanical energy at the rated value. In the controller, the reference value of the pitch angle is predicted by the Adaptive Neuro-Fuzzy Inference System according to the wind speed and the blade tip speed ratio. A proposed fuzzy logic controller provides feedback based on the captured power to modify the pitch angle in real time. The effectiveness of the proposed hybrid pitch angle control method was verified on a 5 MW offshore wind turbine under two different wind conditions using MATLAB/Simulink. The simulation results showed that fluctuations in rotor speed were dramatically mitigated, and the captured mechanical power was always near the rated value as compared with the performance when using the Adaptive Neuro-Fuzzy Inference System alone. The variation rate of power was 0.18% when the proposed controller was employed, whereas it was 2.93% when only an Adaptive Neuro-Fuzzy Inference System was used.

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“…The VAWTs are widely used for they can produce small-scale wind power. The kinetic energy of wind is converted into mechanical energy by the rotational of the wind turbine rotor [4].…”

Section: Introductionmentioning

confidence: 99%

Experimental Study of Vertical Axis Wind Turbine Performance under Vibration

Sarkar

Julai

Nahar

et al. 2021

IJRCS

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An experimental study was conducted to study the effects of flow uniformity on vibration and power generation of a small vertical axis wind turbine (VAWT). Previous studies have confirmed that one of the sources of vibration in the turbine is due to aerodynamic forces, which are due to incident wind. Firstly, understanding vibration is essential before proceeding to the measurements. In this experiment, further understand the vibrations of the turbine in operation, the operating deflection shape (ODS) technique was used. A wind tunnel and flow conditioner were fabricated. Experimental modal analysis (EMA) was conducted, and the dynamic characteristics are gathered. The ODS was conducted for operating the turbine at different speeds, with and without the flow conditioner. Results from EMA and ODS are correlated to explain the behavior of structures. In conclusion, the flow conditioner tested did have a big impact on the response of the structure in terms of vibration up to 30% indifference, but not so much in power generated about 2% indifference.

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Effectiveness of Nature-Inspired Algorithms using ANFIS for Blade Design Optimization and Wind Turbine Efficiency

Cited by 14 publications

References 59 publications

Artificial Neural Network and Adaptive Neuro-Fuzzy Interface System Modelling to Predict Thermal Performances of Thermoelectric Generator for Waste Heat Recovery

Artificial Neural Network and Adaptive Neuro-Fuzzy Interface System Modelling to Predict Thermal Performances of Thermoelectric Generator for Waste Heat Recovery

Smoothing the output power of a wind energy conversion system using a hybrid nonlinear pitch angle controller

Experimental Study of Vertical Axis Wind Turbine Performance under Vibration

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