Using Data-Mining Approaches for Wind Turbine Power Curve Monitoring: A Comparative Study

Schlechtingen, Meik; Santos, Ilmar; Achiche, Sofiane

doi:10.1109/tste.2013.2241797

Cited by 192 publications

(129 citation statements)

References 15 publications

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“…The proposed approaches exhibited small error rates, with values occurring in the range 0.8 m/s to 0.9 m/s. Even though wind speed and output power of a wind generator have a proportional relation (Schlechtingen [7] insisted that temperature and wind direction affect wind power), our study predicts not wind power but long-term hourly wind speed.…”

Section: Wind Power Pattern Forecasting Based On Projected Clusteringmentioning

confidence: 63%

“…Kusiak and others [2] suggested five non-parametric models for monitoring wind farm power; that is, a neural network (NN), M5 tree, representative tree, bagging tree, and k-nearest neighbor (k-NN), the last of which showed the best performance. Unlike existing studies, Schlechtingen and others [7] added ambient temperature and wind direction in addition to wind speed as prediction model inputs, as well as applying four data mining techniques. The applied prediction models were CCFL, NNs, k-NN, and an adaptive neuro-fuzzy interference system (ANFIS).…”

Section: Wind Power Pattern Forecasting Based On Projected Clusteringmentioning

confidence: 99%

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Wind Power Pattern Forecasting Based on Projected Clustering and Classification Methods

Lee¹,

Piao²,

Shin³

2015

ETRI J

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A model that precisely forecasts how much wind power is generated is critical for making decisions on power generation and infrastructure updates. Existing studies have estimated wind power from wind speed using forecasting models such as ANFIS, SMO, k-NN, and ANN. This study applies a projected clustering technique to identify wind power patterns of wind turbines; profiles the resulting characteristics; and defines hourly and daily power patterns using wind power data collected over a year-long period. A wind power pattern prediction stage uses a time interval feature that is essential for producing representative patterns through a projected clustering technique along with the existing temperature and wind direction from the classifier input. During this stage, this feature is applied to the wind speed, which is the most significant input of a forecasting model. As the test results show, nine hourly power patterns and seven daily power patterns are produced with respect to the Korean wind turbines used in this study. As a result of forecasting the hourly and daily power patterns using the temperature, wind direction, and time interval features for the wind speed, the ANFIS and SMO models show an excellent performance.

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Section: Wind Power Pattern Forecasting Based On Projected Clusteringmentioning

confidence: 63%

Section: Wind Power Pattern Forecasting Based On Projected Clusteringmentioning

confidence: 99%

Wind Power Pattern Forecasting Based on Projected Clustering and Classification Methods

Lee¹,

Piao²,

Shin³

2015

ETRI J

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“…Problem Specific Methodology Used [3] 2015 Sea wave Ordinal classification SVM, ANN, LR [4] 2015 Solar Classification SVM [5] 2009 Power disturbance Classification SVM, wavelets [10] 2015 Wind Optimization Bio-inspired, meta-heuristics [14] 2015 Wind Classification Fuzzy SVM [15] 2011 Wind Classification DT, SOM [16] 2015 Wind Classification SVM, k-NN, fuzzy, ANN [17] 2010 Solar Classification Semi-supervised SVM [20] 2013 Wind Ordinal classification SVM, DT, LR, HMM [30] 2014 Wind Classification SVM, LR, RF, rotation forest [31] 2011 Wind Classification ANN, LR, DT, RF [32] 2013 Wind Classification k-NN, RBF, DT [33] 2011 Wind Classification, regression BN [34] 2014 Wind Classification, regression Heuristic methodology: WPPT [35] 2011 Wind Classification Bagging, ripper, rotation forest, RF, k-NN [36] 2013 Wind Classification ANFIS, ANN [37] 2012 Wind Classification SVM [38] 2015 Wind Classification ANN, SVM [39] 2015 Wind Classification PNN [40] 2015 Wind Classification DT, BN, RF [41] 2015 Wind Classification, clustering AuDyC [42] 2016 Wind Classification, clustering AuDyC [43] 2010 Power disturbance Classification HMM, SVM, ANN [44] 2015 Power disturbance Classification SVM, NN, fuzzy, neuro-fuzzy, wavelets, GA [45] 2015 Power disturbance Classification SVM, k-NN, ANN, fuzzy, wavelets [46] 2002 Power disturbance Classification Rule-based classifiers, wavelets, HMM [47] 2004 Power disturbance Classification PNN [48] 2006 Power disturbance Classification ANN, RBF, SVM [49] 2007 Power disturbance Classification ANN, wavelets [50] 2012 Power disturbance Classification PNN [51] 2014 Power disturbance Classification ANN Table 3. Summary of the main references analyzed, grouped by application field, problem type and methodologies considered (II)...…”

Section: Reference Year Application Fieldmentioning

confidence: 99%

A Review of Classification Problems and Algorithms in Renewable Energy Applications

et al. 2016

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Classification problems and their corresponding solving approaches constitute one of the fields of machine learning. The application of classification schemes in Renewable Energy (RE) has gained significant attention in the last few years, contributing to the deployment, management and optimization of RE systems. The main objective of this paper is to review the most important classification algorithms applied to RE problems, including both classical and novel algorithms. The paper also provides a comprehensive literature review and discussion on different classification techniques in specific RE problems, including wind speed/power prediction, fault diagnosis in RE systems, power quality disturbance classification and other applications in alternative RE systems. In this way, the paper describes classification techniques and metrics applied to RE problems, thus being useful both for researchers dealing with this kind of problem and for practitioners of the field.

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“…The WT power curve, which could provide the relationship between power output and wind speed, is one of the most common tools in anomaly detection and performance analysis of WTs [15]. Different SCADA data mining approaches, such as neural networks (NNs) [16,17], adaptive neuron-fuzzy inference systems (ANFIS) [17], k-Nearest Neighbors (k-NN) [17,18], bagging tree [18], and the generalized mapping regressor [19], PC2-Dev [20] were used for modeling the WT power curve and the anomaly detection capabilities of the models were analyzed. It was shown that the abnormal conditions of the WTs can be detected by using the residuals between the predicted power and observed power.…”

Section: Introductionmentioning

confidence: 99%

Condition Assessment for Wind Turbines with Doubly Fed Induction Generators Based on SCADA Data

Sun¹,

Li²,

Wang³

et al. 2017

Journal of Electrical Engineering and Technology

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-This paper presents an effective approach for wind turbine (WT) condition assessment based on the data collected from wind farm supervisory control and data acquisition (SCADA) system. Three types of assessment indices are determined based on the monitoring parameters obtained from the SCADA system. Neural Networks (NNs) are used to establish prediction models for the assessment indices that are dependent on environmental conditions such as ambient temperature and wind speed. An abnormal level index (ALI) is defined to quantify the abnormal level of the proposed indices. Prediction errors of the prediction models follow a normal distribution. Thus, the ALIs can be calculated based on the probability density function of normal distribution. For other assessment indices, the ALIs are calculated by the nonparametric estimation based cumulative probability density function. A Back-Propagation NN (BPNN) algorithm is used for the overall WT condition assessment. The inputs to the BPNN are the ALIs of the proposed indices. The network structure and the number of nodes in the hidden layer are carefully chosen when the BPNN model is being trained. The condition assessment method has been used for real 1.5 MW WTs with doubly fed induction generators. Results show that the proposed assessment method could effectively predict the change of operating conditions prior to fault occurrences and provide early alarming of the developing faults of WTs.

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Using Data-Mining Approaches for Wind Turbine Power Curve Monitoring: A Comparative Study

Cited by 192 publications

References 15 publications

Wind Power Pattern Forecasting Based on Projected Clustering and Classification Methods

Wind Power Pattern Forecasting Based on Projected Clustering and Classification Methods

A Review of Classification Problems and Algorithms in Renewable Energy Applications

Condition Assessment for Wind Turbines with Doubly Fed Induction Generators Based on SCADA Data

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