Abnormal Detection of Wind Turbine Operating Conditions Based on State Curves

Sun, Qunli; Liu, Changliang; Zhen, Chenggang

doi:10.1061/(asce)ey.1943-7897.0000612

Cited by 20 publications

(5 citation statements)

References 25 publications

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“…If the health condition of the OWT changes, there will be some deviation from the normal operation curve. Due to the use of internal variables rather than the meteorological data, the curve between internal variables can reflect the abnormality of single component more accurately than the curve between meteorological data and internal variables [41][42][43]. However, it cannot reflect the relationships between the unit condition and wind power.…”

Section: Variations Of Health Conditions To the Power Curvesmentioning

confidence: 99%

Modeling of Ultra-Short Term Offshore Wind Power Prediction Based on Condition-Assessment of Wind Turbines

Huang

Liu

et al. 2021

Energies

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More accurate wind power prediction (WPP) is of great significance for the operation of electrical power systems, as offshore wind power penetration increases continuously. As the offshore wind turbines (OWT) are a key system in converting offshore wind power into electrical power, maintaining their condition plays a pivotal role in WPP. However, it is seldom considered in traditional WPP. This paper proposes an ultra-short term offshore WPP methodology based on the condition assessment (CA) of OWTs. Firstly, a modified fuzzy comprehensive evaluation (MFCE) based CA of the OWT is presented with a new defined deterioration of indicators calculated by the relative errors. Long short-term memory (LSTM) neural network is introduced to deal with the complicated interactions between the various monitoring data of an OWT and the dynamic marine environment. Then, with the classifications of the health conditions of the OWT, the historical operation data is classified accordingly. An OWT-condition based WPP with a backpropagation (BP) neural network is developed to deal with the non-linear mapping relations between the numerical weather prediction (NWP) information, health conditions of OWT, and the output power. The results of the case study show the influences of the OWT health conditions to its output power and verifies the effectiveness and higher accuracy of the proposed method.

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Section: Variations Of Health Conditions To the Power Curvesmentioning

confidence: 99%

Modeling of Ultra-Short Term Offshore Wind Power Prediction Based on Condition-Assessment of Wind Turbines

Huang

Liu

et al. 2021

Energies

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“…In formula (26), v a is the corresponding fault alarm threshold when the normal interval is set to 70%. The normal interval V % can be set to any value within 0 − 100% according to actual situation.…”

Section: A Threshold Calculationmentioning

confidence: 99%

“…The experimental results showed that the performance of the proposed method is better than the online power curve model and probabilistic assessment using binning. In [26], Sun Q.L. found that using the rotor speed-power and rotor speed-pitch angle curves can accurately monitor the abnormality of WT.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fault Prediction Method of Wind Turbine Gearbox Based on Pair-Copula Construction and BP Neural Network

Luo

Liu

2020

IEEE Access

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Among the various maintenance technologies of wind turbines, online fault prediction technology is a kind of more cost-effective and reliable method. It may also be the most promising method for wind turbines with potential mechanical faults. SCADA data-based online condition monitoring technology has become a hot spot in current researches. Therefore, a novel fault prediction method based on the Pair-Copula model is proposed in this study. First, the conditional mutual information method is introduced to screen out useful variables from a number of variables. Then aiming at the limitation that the conventional Copula model can only deal with two-dimensional variables, the Pair-Copula model is introduced. In addition, the complexity of the prediction model and the dimension of the input variables are greatly reduced by the Pair-Copula model. So, the BP neural network is selected to complete the prediction model. A combined model based on BP neural network and Pair-Copula model is proposed. In order to solve the problem that the conventional Pair-Copula model cannot process real-time data which must be required in fault prediction, a kind of improved Pair-Copula model combined with the kernel density estimation is used to calculate the real-time data. Finally, the proposed method is validated with real data from a 1.5 MW wind turbine, and the effectiveness is confirmed.

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“…WPC modelling of wind turbines is one of the important aspects of wind power prediction, and to improve the accuracy of power prediction, we need to continuously enhance the precision of wind power curve (WPC) modelling. [2][3][4][5] The standard WPC of wind turbines is a curve describing the relationship between wind speed and output power per unit time. Due to the influence of turbulence intensity, air density, wind shear and other environmental factors, the WPC under actual conditions is different from the standard power curve provided by the manufacturer.…”

Section: Introductionmentioning

confidence: 99%

Power curve modelling for wind turbines and wind power prediction based on mixed Richards model

Wang,

Chen,

Wang

2024

Preprint

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Accurate modelling of wind speed-power curve of wind turbines plays an important role in wind power prediction, state detection and performance evaluation. While model selection is one of the keys to improve the accuracy of wind power curve (WPC) modelling. To improve the accuracy of models and accurately characterize the overall output behavior of wind turbines, a method of WPC modelling based on the mixed Richards model is proposed in this paper. By using the measured data of two wind fields, the method proposed in this paper is compared and verified with the sixth to ninth order polynomials and the four-parameter and five-parameter logistic function models based on the genetic least square method through five indicators include the root mean squared error, the coefficient of determination R2, the mean absolute percentage error, the improved Akaike information criterion and the Bayesian information criterion. Finally, based on the measured data of a wind field in Jiangsu Province, the two-fold mixed Richards model is used to predict the wind turbine power. The results show that the two-fold mixed Richards model is the optimal option with the highest fitting accuracy, effectively avoids the model’s over-fitting, and can accurately predict wind turbine output power.

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Abnormal Detection of Wind Turbine Operating Conditions Based on State Curves

Cited by 20 publications

References 25 publications

Modeling of Ultra-Short Term Offshore Wind Power Prediction Based on Condition-Assessment of Wind Turbines

Modeling of Ultra-Short Term Offshore Wind Power Prediction Based on Condition-Assessment of Wind Turbines

A Novel Fault Prediction Method of Wind Turbine Gearbox Based on Pair-Copula Construction and BP Neural Network

Power curve modelling for wind turbines and wind power prediction based on mixed Richards model

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