Data-driven methods for prediction of small-to-medium wind turbines performance

Data-driven methods for prediction of small-to-medium wind turbines performance

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The growth in the wind energy is rapidly increasing. Accurate modelling of wind turbines performance as targeted by ongoing research studies can escalate wind energy production capabilities, reliability, and expand its potential to replace fossil fuels. In addition, optimisation of turbines will considerably expand the profit margins and garner the attraction of investors.

Chapter Contents:

  • Abstract
  • 2.1 Introduction
  • 2.2 SCADA data treatment
  • 2.2.1 Mean or median value
  • 2.2.2 K-Nearest neighbour
  • 2.2.3 Expectation–maximisation
  • 2.2.4 Decision tree
  • 2.3 Feature selection
  • 2.3.1 Correlation coefficients
  • 2.3.2 Principal component analysis
  • Off-line PCA
  • Recurrent PCA
  • 2.4 Modelling design networks
  • 2.4.1 Multi-layer perceptron
  • 2.4.2 Adaptive neuro-fuzzy inference system
  • 2.4.3 Static and dynamic networks
  • 2.4.4 Fusion
  • 2.4.5 Estimation and prediction
  • 2.4.6 Performance evaluation
  • 2.5 A case study
  • 2.5.1 Data pre-processing
  • Data range
  • Outlier detection
  • Missing values
  • 2.5.2 Monitoring networks
  • 2.6 Conclusion
  • References

Inspec keywords: power generation reliability; wind turbines; wind power plants

Other keywords: wind energy production capabilities; small-to-medium wind turbine performance prediction; reliability; fossil fuels; data-driven methods; profit margins

Subjects: Wind power plants; Reliability

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