Simplified automatic fault detection in wind turbine induction generators
| Autor: | Donatella Zappalá, Christopher J. Crabtree, Katharine Brigham, Christopher Donaghy-Spargo |
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| Rok vydání: | 2020 |
| Předmět: |
Imagination
Wind power Renewable Energy Sustainability and the Environment Computer science business.industry 020209 energy media_common.quotation_subject Induction generator Condition monitoring 02 engineering and technology 01 natural sciences Turbine Fault detection and isolation 010305 fluids & plasmas Band-pass filter Control theory Harmonics 0103 physical sciences 0202 electrical engineering electronic engineering information engineering business media_common |
| Zdroj: | Wind energy, 2020, Vol.23(4), pp.1135-1144 [Peer Reviewed Journal] |
| Popis: | This paper presents a simplified automated fault detection scheme for wind turbine induction generators with rotor electrical asymmetries. Fault indicators developed in previous works have made use of the presence of significant spectral peaks in the upper sidebands of the supply frequency harmonics; however, the specific location of these peaks may shift depending on the wind turbine speed. As wind turbines tend to operate under variable speed conditions, it may be difficult to predict where these fault‐related peaks will occur. To accommodate for variable speeds and resulting shifting frequency peak locations, previous works have introduced methods to identify or track the relevant frequencies, which necessitates an additional set of processing algorithms to locate these fault‐related peaks prior to any fault analysis. In this work, a simplified method is proposed to instead bypass the issue of variable speed (and shifting frequency peaks) by introducing a set of bandpass filters that encompass the ranges in which the peaks are expected to occur. These filters are designed to capture the fault‐related spectral information to train a classifier for automatic fault detection, regardless of the specific location of the peaks. Initial experimental results show that this approach is robust against variable speeds and further shows good generalizability in being able to detect faults at speeds and conditions that were not presented during training. After training and tuning the proposed fault detection system, the system was tested on “unseen” data and yielded a high classification accuracy of 97.4%, demonstrating the efficacy of the proposed approach. |
| Databáze: | OpenAIRE |
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