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.

基于稀疏自编码器（SAE）- 生成对抗网络（GAN）- 小波降噪（WNR）- 流形增强深度学习（SG-WMBDL）的水电站故障定位方法，通过提供高质量样本数据、降噪和捕捉局部特征，结合自适应增强的多深度学习，实现了具有非线性和非平滑特性的水电站故障的精准定位。