The quest for green energy has motivated the world over to see the potential of wind as an important source of energy. The wind power industry is rapidly developing and therefore there is an increasing need to lower the Cost of Energy. Wind farms are being put up each day around the world in places of high wind energy potential so as to harness the renewable power of the wind. However, ageing of wind turbines and their components is inevitable. Over time, ageing will affect the reliability and power generation efficiency of the turbine. Therefore, performing an ageing assessment of the wind turbines is of significance so as to not only optimize the operation and maintenance strategy of the turbine, but also improve the management of the wind farm. Little has so far been done on this ageing led performance degradation concern of the turbine since most of the existing turbine monitoring techniques are mainly focused on condition monitoring for fault detection purposes.
An Innovative research was organized by Newcastle University in the UK, in collaboration with Hunan University of Science and Technology, Hunan Institute of Engineering, and XEMC Windpower Co. Ltd in China, to investigate the ageing issue of onshore wind turbines over time through interpreting the data collected by the wind farm Supervisory Control and Data Acquisition (SCADA) system. The research aimed at assessing the decline in performance of the turbines that could be directly attributed to their ageing. This research work is now published in the peer-reviewed journal, Renewable Energy.
The research team started by discussing the SCADA parameters that potentially could be used for the ageing assessment. The team then developed four ageing assessment criteria so as to describe the ageing issues of wind turbines from various viewpoints. From these, they were able to develop an innovative information fusion based ageing assessment method. Eventually, the effectiveness of the proposed method was verified using real SCADA data collected from an onshore wind farm.
The team observed that the values of the four ageing criteria deviated from 1. Since it is clear that during the turbines’ service life ageing will inevitably happen, the separate analysis of the individual ageing assessment criterion could not lead to reliable results regarding a turbines’ ageing effect. Therefore, the conventional individual assessment technique failed to yield reliable assessment results. However, the team noted that when the information fusion-based method was applied using the four ageing criteria, more realistic and acceptable results were obtained. The value of the information fusion based criterion was noted to deviate well from 1 in presence of ageing over time.
Herein, the conventional individual-based turbine age assessment method and the comprehensive information fusion-based method have been applied hand in hand. The latter technique exhibits reliability and robustness in assessing a turbines performance decline with age. It can therefore be recommended for use in such related works as its estimates from computations are quite reliable.
Juchuan Dai, Wenxian Yang, Junwei Cao, Deshun Liu, Xing Long. Ageing assessment of a wind turbine over time by interpreting wind farm SCADA data. Renewable Energy, Available online 31 March 2017.Go To Renewable Energy