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| A multi-timescale-based early fault monitoring method for wind turbine gearboxes |
| DOI：10.19783/j.cnki.pspc.260282 |
| Key Words:wind turbine SCADA data fault monitoring gated recurrent unit time series forecasting exponentially weighted moving average |
| Author Name | Affiliation | | ZHANG Cheng | 1. School of Electronic Electrical and Physics, Fujian University of Technology, Fuzhou 350118, China
2. Fujian Provincal University Engineering Research Center for Simulation Analysis and Integrated Control of Smart Grid, Fuzhou 350118, China
3. Ningde Power Supply Company, State Grid Fujian Electric Power Co., Ltd., Ningde 352100, China | | LEI Buchen | 1. School of Electronic Electrical and Physics, Fujian University of Technology, Fuzhou 350118, China
2. Fujian Provincal University Engineering Research Center for Simulation Analysis and Integrated Control of Smart Grid, Fuzhou 350118, China
3. Ningde Power Supply Company, State Grid Fujian Electric Power Co., Ltd., Ningde 352100, China | | YU Shilin | 1. School of Electronic Electrical and Physics, Fujian University of Technology, Fuzhou 350118, China
2. Fujian Provincal University Engineering Research Center for Simulation Analysis and Integrated Control of Smart Grid, Fuzhou 350118, China
3. Ningde Power Supply Company, State Grid Fujian Electric Power Co., Ltd., Ningde 352100, China | | LU Wanlin | 1. School of Electronic Electrical and Physics, Fujian University of Technology, Fuzhou 350118, China
2. Fujian Provincal University Engineering Research Center for Simulation Analysis and Integrated Control of Smart Grid, Fuzhou 350118, China
3. Ningde Power Supply Company, State Grid Fujian Electric Power Co., Ltd., Ningde 352100, China | | CHEN Changliang | 1. School of Electronic Electrical and Physics, Fujian University of Technology, Fuzhou 350118, China
2. Fujian Provincal University Engineering Research Center for Simulation Analysis and Integrated Control of Smart Grid, Fuzhou 350118, China
3. Ningde Power Supply Company, State Grid Fujian Electric Power Co., Ltd., Ningde 352100, China |
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| Abstract:To address the challenges of fault identification in wind turbines caused by weak early fault features and variable operating conditions in SCADA data, this paper proposes an early fault monitoring method for wind turbine gearboxes based on parallel gated recurrent unit (P-GRU) and multi-timescale exponentially weighted moving average (MTS-EWMA). First, an overlapping sliding window sampling (OSWS) strategy is employed to reconstruct the SCADA multivariate time series to fully preserve temporal information. Subsequently, a P-GRU prediction model is constructed, and through its parallel temporal feature extraction architecture, the model’s capability to learn multivariate dynamic correlation features under complex operating conditions is enhanced. Finally, an MTS-EWMA monitoring framework is constructed to fuse prediction residuals from different time scales, thereby enabling sensitive detection of slowly evolving faults while suppressing false alarms. Experimental results demonstrate that the proposed method can provide stable early warnings before actual wind turbine faults, thereby verifying its effectiveness for early fault monitoring of wind turbine gearboxes. |
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