Abstract
Accurate online diagnosis of incipient faults and condition assessment on generators is especially challenging to automate through supervised learning techniques, because of data imbalance. Fault-condition training and test data are either not available or are experimentally emulated, and therefore do not precisely account for all the eventualities and nuances of practical operating conditions. Thus, it would be more convenient to harness the ability of unsupervised learning in these applications. An investigation into the use of unsupervised learning as a means of recognizing incipient fault patterns and assessing the condition of a wound-rotor induction generator is presented. High-dimension clustering is performed using stator and rotor current and voltage signatures measured under healthy and varying fault conditions on an experimental wound-rotor induction generator. An analysis and validation of the clustering results are carried out to determine the performance and suitability of the technique. Results indicate that the presented technique can accurately distinguish the different incipient faults investigated in an unsupervised manner. This research will contribute to the ongoing development of unsupervised learning frameworks in data-driven diagnostic systems for WRIGs and similar electrical machines.
Highlights
IntroductionMore attention is being given to research of wound-rotor induction generator (WRIG) condition monitoring methods
The need for improved accuracy and flexibility in condition monitoring approaches on electrical machines has prompted the transition from model-based methods to datadriven approaches
In an effort towards overcoming these problems, this study investigates the suitability of an unsupervised learning approach in accurately diagnosing incipient faults on a wound-rotor induction generator (WRIG)
Summary
More attention is being given to research of wound-rotor induction generator (WRIG) condition monitoring methods. This is due to the growing interest in the use of the WRIG for wind-turbine applications because of its desirable traits such as dynamic control and relatively robust performance. The classifier is trained with known data so that it can predict, or classify, the unknown instances. Semi-supervised learning uses the labeled data from a smaller subset of the data to identify and label other data in order to subsequently retrain the model. Reinforcement learning interacts with dynamic environment to achieve objectives based on rewards and penalities [15]
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