An adaptive neuro-fuzzy inference system approach for prediction of tip speed ratio in wind turbines

Ata, Raşit; Koçyiğit, Yücel

doi:10.1016/j.eswa.2010.02.068

Cited by 83 publications

(47 citation statements)

References 10 publications

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“…Furthermore, λ dgn corresponding to the maximum C p,max is selected according to the functional relation between λ dgn and C p,max , which, however, is derived based on single-airfoil wind turbine rotors [24][25][26][27]. Because it is hard to deduce the analytical relationship between λ dgn and C p,max for multi-airfoil wind turbine rotors, in implementing the inverse design method in this paper, λ dgn is chosen as 8.5 according to the analysis result presented in Section 2.1.…”

Section: Determination Of λ Dgn For the Inverse Design Methodsmentioning

confidence: 99%

“…For single-airfoil wind turbine rotors, the relationship between λdgn and Cp,max characterized by a parabolic function has been derived in References [24][25][26]. Furthermore, for multi-airfoil wind turbine rotors, which are generally implemented in practical VSWTs, the 1.5 MW wind partnership for advanced component technologies (WindPACT) turbine [37] developed by the national renewable energy laboratory (NREL), is chosen for analyzing the relation of Cp,max with λdgn.…”

Section: Relationship Between λDgn and Cpmaxmentioning

confidence: 99%

“…To obtain an appropriate λ dgn , the functional relation between λ dgn and the maximum power coefficient C p,max , which is regarded as a type of the turbines' static aerodynamic performance, has been discussed in References [24][25][26][27]. According to this relation, the λ dgn corresponding to the maximum value of C p,max is selected and then the chord and twist distributions are optimized based on this λ dgn by an inverse method in Reference [13].…”

Section: Introductionmentioning

confidence: 99%

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Inverse Aerodynamic Optimization Considering Impacts of Design Tip Speed Ratio for Variable-Speed Wind Turbines

Yang

Zou

et al. 2016

Energies

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Abstract:Because of the slow dynamic behavior of the large-inertia wind turbine rotor, variable-speed wind turbines (VSWTs) are actually unable to keep operating at the design tip speed ratio (TSR) during the maximum power point tracking (MPPT) process. Moreover, it has been pointed out that although a larger design TSR can increase the maximum power coefficient, it also greatly prolongs the MPPT process of VSWTs. Consequently, turbines spend more time operating at the off-design TSRs and the wind energy capture efficiency is decreased. Therefore, in the inverse aerodynamic design of VSWTs, the static aerodynamic performance (i.e., the maximum power coefficient) and the dynamic process of MPPT should be comprehensively modeled for determining an appropriate design TSR. In this paper, based on the inverse design method, an aerodynamic optimization method for VSWTs, fully considering the impacts of the design TSR on the static and dynamic behavior of wind turbines is proposed. In this method, to achieve higher wind energy production, the design TSR, chord length and twist angle are jointly optimized, which is structurally different from the conventional separated design procedure. Finally, the effectiveness of the proposed method is validated by simulation results based on the Bladed software.

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Section: Determination Of λ Dgn For the Inverse Design Methodsmentioning

confidence: 99%

Section: Relationship Between λDgn and Cpmaxmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Inverse Aerodynamic Optimization Considering Impacts of Design Tip Speed Ratio for Variable-Speed Wind Turbines

Yang

Zou

et al. 2016

Energies

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“…It was found that the prediction performances of ANFIS model is better than traditional multiple linear regression model. ANFIS has also been used in the field of science and technology by many researchers (Guler & Ubeyli, 2004;Zaheeruddin & Garima, 2006;Naadimutha et al, 2007;Cakmakci, 2007;Bakhtyar et al, 2008;Wang & Elhag, 2008;Khajeh et al, 2009;Radulovic & Rankovic, 2010;Yan et al, 2010;Ata & Kocyigit, 2010;Sargolzaei & Kianifar, 2010;Yilmaz & Kaynar, 2011;Mohammadi et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Backbreak in Open Pit Blasting by Adaptive Neuro-Fuzzy Inference System / Prognozowanie Spękań Skał Przy Pracach Strzałowych W Kopalniach Odkrywkowych Przy Użyciu Metod Neuronowych I Wnioskowania Rozmytego (Anfis) Zastosowanych W Modelu Adaptywnym

Bazzazi¹,

Esmaeili²

2012

Archives of Mining Sciences

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Adaptive neuro-fuzzy inference system (ANFIS) is powerful model in solving complex problems. Since ANFIS has the potential of solving nonlinear problem and can easily achieve the input-output mapping, it is perfect to be used for solving the predicting problem. Backbreak is one of the undesirable effects of blasting operations causing instability in mine walls, falling down the machinery, improper fragmentation and reduction in efficiency of drilling. In this paper, ANFIS was applied to predict backbreak in Sangan iron mine of Iran. The performance of the model was assessed through the root mean squared error (RMSE), the variance account for (VAF) and the correlation coefficient (R 2 ) computed from the measured of backbreak and model-predicted values of the dependent variables. The RMSE, VAF, R 2 indices were calculated 0.6, 0.94 and 0.95 for ANFIS model. As results, these indices revealed that the ANFIS model has very good prediction performance.Keywords: blasting, backbreak, adaptive neuro-fuzzy inference system, Sangan iron mine Adaptywny system wnioskowania wykorzystujący elementy sieci neuronowych i logiki rozmytej (AN-FIS) stanowi potężny narzędzie do rozwiązywania złożonych problemów. Ponieważ model ANFIS może być wykorzystywany do rozwiązywania problemów nieliniowych i umożliwia wygodne przedstawienie problemu w formie: wejście -wyjście, jest idealnym narzędziem do rozwiązywania problemów związanych z prognozowaniem. Pękanie skał w odkrywce jest jednym z niekorzystnych skutków prowadzenia prac strzałowych, powoduje niestabilność ścian, uszkodzenia maszyn i urządzeń, nieodpowiednią fragmentację skał oraz prowadzi do obniżenia efektywności wierceń. W pracy przedstawiono zastosowanie systemu ANFIS do prognozowania pękań skał w kopalni rud żelaza w Sangan (Iran). Działanie modelu zbadano na podstawie wartości błędu średniokwadratowego (RMSE), wariancji (VAF) i współczynnika korelacji Słowa kluczowe: prace strzałowe, pękanie skał, system wnioskowania wykorzystujący elementy sieci neuronowych i logiki rozmytej, kopalnia rud żelaza Sangan IntroductionAlthough the main purpose of blasting in open pit mines is rock breakage and finally facilitating in loading operations, the other effects of blasting such as ground vibration, fly rock and backbreak should be considered. Backbreak can be defined as breakage behind the last row of holes (Konya & Walter, 1991). This phenomenon may cause instability in mine walls, falling down the machinery, improper fragmentation and reduction in efficiency of drilling (Bazzazi, 2005). Several factors leading to backbreak have been described by various researchers. Konya and Walter (1991) described some of the causes for backbreak such as excessive burden and stiff benches, long stemming depth on stiff benches and improper timing delay. Gate et al. (2005) believed that a combination of factors in the blasting such as overstemming of the shot holes and short timing delays in the firing sequence may have led to the severe backbreak. Moreover, the adverse geological structure app...

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“…The neuro-fuzzy system includes training capabilities while retaining the advantages of the fuzzy inference system [16].  Unlike other systems based on fuzzy logic, there is no need to convert crisp data to fuzzy data and initialize the parameters.…”

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confidence: 99%

An Intelligent Anti-Money Laundering Method for Detecting Risky Users in the Banking Systems

Heidarinia¹,

Harounabadi²,

Sadeghzadeh³

2014

IJCA

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During the last decades, universal economy has experienced money laundering and its destructive impact on the economy of the countries. Money laundering is the process of converting or transferring an asset in order to conceal its illegal source or assist someone that is involved in such crimes. Criminals generally attempt to clean the sources of the funds obtained by crime, using the banking system. Due to the large amount of information in the banks, detecting such behaviors is not feasible without anti-money laundering systems. Money laundering detection is one of the areas, where data mining tools can be useful and effective.In this research, some of the features of the users are extracted from their profiles by studying them. These features may include large financial transactions in risky areas regarding money laundering, reactivation of dormant accounts with considerable amounts, etc. Network training is performed by designing a fuzzy system, developing an adaptive neuro-fuzzy inference system and adding feature vectors of the users to it. The network output can determine the riskiness of the user behavior. The evaluation results reveal that the proposed method increases the accuracy of detecting risky users.

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An adaptive neuro-fuzzy inference system approach for prediction of tip speed ratio in wind turbines

Cited by 83 publications

References 10 publications

Inverse Aerodynamic Optimization Considering Impacts of Design Tip Speed Ratio for Variable-Speed Wind Turbines

Inverse Aerodynamic Optimization Considering Impacts of Design Tip Speed Ratio for Variable-Speed Wind Turbines

Prediction of Backbreak in Open Pit Blasting by Adaptive Neuro-Fuzzy Inference System / Prognozowanie Spękań Skał Przy Pracach Strzałowych W Kopalniach Odkrywkowych Przy Użyciu Metod Neuronowych I Wnioskowania Rozmytego (Anfis) Zastosowanych W Modelu Adaptywnym

An Intelligent Anti-Money Laundering Method for Detecting Risky Users in the Banking Systems

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