A Data-Mining Approach for Wind Turbine Power Generation Performance Monitoring Based on Power Curve

2016 ◽  
Vol 10 (2) ◽  
pp. 137-152 ◽  
Author(s):  
Jianlou Lou ◽  
Heng Lu ◽  
Jia Xu ◽  
Zhaoyang Qu
Keyword(s):  
Data Mining ◽  
Power Generation ◽  
Wind Turbine ◽  
Performance Monitoring ◽  
Power Curve ◽  
Data Mining Approach ◽  
Power Generation Performance

Data-Driven Wind Turbine Power Generation Performance Monitoring

2015 ◽  
Vol 62 (10) ◽  
pp. 6627-6635 ◽  
Author(s):  
Huan Long ◽  
Long Wang ◽  
Zijun Zhang ◽  
Zhe Song ◽  
Jia Xu
Keyword(s):  
Power Generation ◽  
Wind Turbine ◽  
Performance Monitoring ◽  
Data Driven ◽  
Power Generation Performance

Output Power Modeling of Wind Turbine Based on State Curve Analysis

2021 ◽  
Vol 02 ◽  
Author(s):  
Jiaying Huang ◽  
Wangqiang Niu ◽  
Xiaotong Wang
Keyword(s):  
Power Generation ◽  
Feature Selection ◽  
Wind Turbine ◽  
Polynomial Regression ◽  
Modeling Method ◽  
Curve Analysis ◽  
Mechanical Transmission ◽  
Power Curve ◽  
Power Modeling ◽  
State Curve

Background: In wind power generation, the power curve can reflect the overall power generation performance of a wind turbine. How to make the power curve have high precision and be easy to interpret is a hot research topic. Objective: Because the current power curve modeling method is not comprehensive in feature selection, the simplified model and state curve of a wind turbine are introduced to avoid feature selection and make the model interpret easily. Methods: A power modeling method based on different working conditions is proposed. The wind turbine system is simplified into three physical models of blades, mechanical transmission and generator, and the energy transfer is expressed by mathematical expressions. The operation process of the wind turbine is divided into three phases: constant power (CP), constant speed (CS), and maximum power point tracking (MPPT), and the power expression of each phase is given after the analysis of state curves. Results: The effectiveness of the proposed method is verified by the supervisory control and data acquisition (SCADA) data of a 2MW wind turbine. The experimental results show that the mean absolute percentage error (MAPE) index of the proposed power modeling method based on state curve analysis is 11.56%, which indicates that the power prediction result of this method is better than that of the sixth-order polynomial regression method, whose MAPE is 13.88%. Conclusion: The results show that the proposed method is feasible with high transparency and is interpreted easily.

scholarly journals Performance Evaluation and Prediction of BIPV Systems under Partial Shading Conditions Using Normalized Efficiency

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3777
Author(s):  
Chul-sung Lee ◽  
Hyo-mun Lee ◽  
Min-joo Choi ◽  
Jong-ho Yoon
Keyword(s):  
Power Generation ◽  
Electricity Generation ◽  
Performance Monitoring ◽  
Building Energy ◽  
Building Envelope ◽  
Partial Shading ◽  
Pv Systems ◽  
Surrounding Environment ◽  
Building Integrated Photovoltaic ◽  
Power Generation Performance

The performance of the Operable Building Integrated Photovoltaic (OBIPV) system applied to the building envelope to reduce the building energy consumption varies significantly depending on the operation method and influence of the surrounding environment. Therefore, optimization through performance monitoring is necessary to maximize power generation of the system. This study used temperature-corrected normalized efficiency (NE*) to evaluate the power generation performance of the operation methods and predict that of the OBIPV system based upon the measured data. It was confirmed that power generation performance decreased when the photovoltaic (PV) operation angle changed, the system remaining the same. A decrease in power generation performance due to partial shading from an overhang was also observed. As a result of the power generation prediction for two months using NE*, the error of the measured values was found to be less than 3%. In addition, with or without any partial shading of the OBIPV system, its performance degradation was predicted with an annual electricity generation decrease by 36 kWh/yr (6.5%). Therefore, NE* can be used as an indicator for evaluating the power generation performance of PV systems, and to predict generation performance considering partial shading.

Fault Monitoring of Wind Turbine Generator Brushes: A Data-Mining Approach

2012 ◽  
Vol 134 (2) ◽  
Author(s):  
Anoop Verma ◽  
Andrew Kusiak
Keyword(s):  
Data Mining ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Turbine Generator ◽  
Wind Turbine Generator ◽  
Data Mining Algorithms ◽  
Data Mining Approach ◽  
Carbon Brushes ◽  
Critical Components ◽  
Mining Algorithms

Components of wind turbines are subjected to asymmetric loads caused by variable wind conditions. Carbon brushes are critical components of the wind turbine generator. Adequately maintaining and detecting abnormalities in the carbon brushes early is essential for proper turbine performance. In this paper, data-mining algorithms are applied for early prediction of carbon brush faults. Predicting generator brush faults early enables timely maintenance or replacement of brushes. The results discussed in this paper are based on analyzing generator brush faults that occurred on 27 wind turbines. The datasets used to analyze faults were collected from the supervisory control and data acquisition (SCADA) systems installed at the wind turbines. Twenty-four data-mining models are constructed to predict faults up to 12 h before the actual fault occurs. To increase the prediction accuracy of the models discussed, a data balancing approach is used. Four data-mining algorithms were studied to evaluate the quality of the models for predicting generator brush faults. Among the selected data-mining algorithms, the boosting tree algorithm provided the best prediction results. Research limitations attributed to the available datasets are discussed.

scholarly journals Wind Turbine Power Curve Design for Optimal Power Generation in Wind Farms Considering Wake Effect

Energies ◽  
2017 ◽  
Vol 10 (3) ◽  
pp. 395 ◽  
Author(s):  
Jie Tian ◽  
Dao Zhou ◽  
Chi Su ◽  
Mohsen Soltani ◽  
Zhe Chen ◽  
...  
Keyword(s):  
Power Generation ◽  
Wind Turbine ◽  
Wind Farms ◽  
Power Curve ◽  
Wake Effect ◽  
Curve Design ◽  
Optimal Power

scholarly journals Multivariate Wind Turbine Power Curve Model Based on Data Clustering and Polynomial LASSO Regression

2021 ◽  
Vol 12 (1) ◽  
pp. 72
Author(s):  
Davide Astolfi ◽  
Ravi Pandit
Keyword(s):  
Wind Turbine ◽  
Data Clustering ◽  
Performance Monitoring ◽  
Power Curve ◽  
Lasso Regression ◽  
Data Set ◽  
Power Monitoring ◽  
Turbine Performance ◽  
Input Variables ◽  
Power Curves

Wind turbine performance monitoring is a complex task because of the non-stationary operation conditions and because the power has a multivariate dependence on the ambient conditions and working parameters. This motivates the research about the use of SCADA data for constructing reliable models applicable in wind turbine performance monitoring. The present work is devoted to multivariate wind turbine power curves, which can be conceived of as multiple input, single output models. The output is the power of the target wind turbine, and the input variables are the wind speed and additional covariates, which in this work are the blade pitch and rotor speed. The objective of this study is to contribute to the formulation of multivariate wind turbine power curve models, which conjugate precision and simplicity and are therefore appropriate for industrial applications. The non-linearity of the relation between the input variables and the output was taken into account through the simplification of a polynomial LASSO regression: the advantages of this are that the input variables selection is performed automatically. The k-means algorithm was employed for automatic multi-dimensional data clustering, and a separate sub-model was formulated for each cluster, whose total number was selected by analyzing the silhouette score. The proposed method was tested on the SCADA data of an industrial Vestas V52 wind turbine. It resulted that the most appropriate number of clusters was three, which fairly resembles the main features of the wind turbine control. As expected, the importance of the different input variables varied with the cluster. The achieved model validation error metrics are the following: the mean absolute percentage error was in the order of 7.2%, and the average difference of mean percentage errors on random subsets of the target data set was of the order of 0.001%. This indicates that the proposed model, despite its simplicity, can be reliably employed for wind turbine power monitoring and for evaluating accumulated performance changes due to aging and/or optimization.

scholarly journals Performance Analysis and Structure Optimization of a Nautilus Isometric Spiral Wind Turbine

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 120
Author(s):  
Zheng Li ◽  
Wenda Zhang ◽  
Hao Dong ◽  
Yongsheng Tian
Keyword(s):  
Power Generation ◽  
Control System ◽  
Wind Turbine ◽  
Aerodynamic Performance ◽  
Energy Utilization ◽  
Simulation Platform ◽  
Grid Connection ◽  
Power Generation Control ◽  
Generation Control ◽  
Power Generation Performance

Background: This paper proposes a Nautilus isometric spiral vertical axis wind turbine, which is a new structure, and its aerodynamic performance and power generation performance need to be analyzed. Methods: A 3D model of the wind turbine was built and its aerodynamic performance was analyzed. Then the wind turbine power generation and grid-connected simulation platform was built by MATLAB/SIMULINK, and its power generation performance and subsequent grid connection were studied. Results: The basic parameters of the wind turbine were obtained. In order to improve efficiency, parameters such as pressure, torque, wind energy utilization rate and relative velocity of wind turbines with different blade numbers and different sizes were compared. In addition, by building a simulation platform for the power generation control system, the power generation characteristics and grid connection characteristic curves of the generator were obtained. Conclusions: When the number of blades is three and the ratio between the ellipse major axis and minor axis of the blade inlet is 0.76, the best efficiency of the wind turbine can be obtained. Application of the power generation control system used in this paper can achieve grid-connected operation of this wind turbine. It also confirmed that the Nautilus isometric spiral wind turbine has good performance and is worthy of in-depth research.

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