Abstract
This paper presents an approach for identification of wind power conversion systems based on related real data from the wind farm supervisory control and data acquisition (SCADA) module. The proposed method is able to determine the order of the system more accurately and identify the system parameters more effectively, and such improvement is accomplished in two steps. The first step is to use a hybrid algorithm based on singular entropy increment (SEI) and weighted criterion to overcome the uncertainty and arbitrariness of traditional data based order determination methods. The second step is to blend SEI analysis and eigensystem realization algorithm (ERA) with Kalman filter to improve the parameter estimation accuracy in the presence of white noise inevitable in wind turbine systems. As a validation of the developed method, the SKYSTREAM 3.7 is used as a benchmark for simulation experiments, which demonstrate that the proposed hybrid scheme leads to good performance in wind power system order determination and parameter identification. © 2011 American Institute of Physics.
| Original language | English |
|---|---|
| Article number | 033107 |
| Journal | Journal of Renewable and Sustainable Energy |
| Volume | 3 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - May 2011 |
| Externally published | Yes |
Bibliographical note
The authors gratefully acknowledge conversations with Dr. Feng Liu concerning this work.Funding
This work is supported in part by the National Natural Science Foundation of China under Grant No. 60974052. and Key Laboratory of Condition Monitoring and Control for Power Plant Equipment of North China Electric Power University.
