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

  • Sun, Peng (State Grid Henan Electrical Power Research Institute) ;
  • Li, Jian (State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University) ;
  • Wang, Caisheng (Department of Electrical and Computer Engineering, Wayne State University) ;
  • Yan, Yonglong (State Grid Wulumuqi Electric Supply Company)
  • Received : 2016.04.19
  • Accepted : 2016.10.28
  • Published : 2017.03.01

Abstract

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.

Keywords

Condition assessment;Prediction model;Neural network;SCADA data;Wind turbine

Acknowledgement

Supported by : National Natural Science Foundation of China, National Science Foundation of USA

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