An End-to-End: Real-Time Solution for Condition Monitoring of Wind Turbine Generators

Energies

John Keane¹  Goran Nenadic¹  Adrian Stetco¹  Siniša Djurović²  JuanMelecio Ramirez²  Anees Mohammed² 

[1] Department of Computer Science, University of Manchester, Manchester M13 9PL, UK;Department of Electrical and Electronic Engineering, University of Manchester, Manchester M1 3BB, UK;

关键词: wind turbine;    real-time diagnostic;    generator;    convolutional neural networks;    condition monitoring;   

DOI  :  10.3390/en13184817

来源: DOAJ

【 摘 要 】

Data-driven wind generator condition monitoring systems largely rely on multi-stage processing involving feature selection and extraction followed by supervised learning. These stages require expert analysis, are potentially error-prone and do not generalize well between applications. In this paper, we introduce a collection of end-to-end Convolutional Neural Networks for advanced condition monitoring of wind turbine generators. End-to-end models have the benefit of utilizing raw, unstructured signals to make predictions about the parameters of interest. This feature makes it easier to scale an existing collection of models to new predictive tasks (e.g., new failure types) since feature extracting steps are not required. These automated models achieve low Mean Squared Errors in predicting the generator operational state (40.85 for Speed and 0.0018 for Load) and high accuracy in diagnosing rotor demagnetization failures (99.67%) by utilizing only raw current signals. We show how to create, deploy and run the collection of proposed models in a real-time setting using a laptop connected to a test rig via a data acquisition card. Based on a sampling rate of 5 kHz, predictions are stored in an efficient time series database and monitored using a dynamic visualization framework. We further discuss existing options for understanding the decision process behind the predictions made by the models.

【 授权许可】

Unknown