A novel fault localization with data refinement for hydroelectric units

TL;DR

This paper introduces a novel deep learning-based fault localization method for hydroelectric units that effectively handles data scarcity and complex signal characteristics, achieving higher accuracy than existing methods.

Contribution

It proposes an integrated approach combining SAE-GAN, wavelet noise reduction, and adaptive boosting to improve fault localization in hydroelectric units with limited fault samples.

Findings

Achieves higher localization accuracy compared to existing methods.

Effectively handles non-linear and non-smooth signal characteristics.

Demonstrates robustness with small fault sample datasets.

Abstract

Due to the scarcity of fault samples and the complexity of non-linear and non-smooth characteristics data in hydroelectric units, most of the traditional hydroelectric unit fault localization methods are difficult to carry out accurate localization. To address these problems, a sparse autoencoder (SAE)-generative adversarial network (GAN)-wavelet noise reduction (WNR)- manifold-boosted deep learning (SG-WMBDL) based fault localization method for hydroelectric units is proposed. To overcome the data scarcity, a SAE is embedded into the GAN to generate more high-quality samples in the data generation module. Considering the signals involving non-linear and non-smooth characteristics, the improved WNR which combining both soft and hard thresholding and local linear embedding (LLE) are utilized to the data preprocessing module in order to reduce the noise and effectively capture the local features. In addition, to seek higher performance, the novel Adaptive Boost (AdaBoost) combined with multi deep learning is proposed to achieve accurate fault localization. The experimental results show that the SG-WMBDL can locate faults for hydroelectric units under a small number of fault samples with non-linear and non-smooth characteristics on higher precision and accuracy compared to other frontier methods, which verifies the effectiveness and practicality of the proposed method.