SGAIN, WSGAIN-CP and WSGAIN-GP: Novel GAN Methods for Missing Data Imputation

Neves, Diogo Telmo; Naik, Marcel; Proença, Alberto José

doi:10.1007/978-3-030-77961-0_10

Cited by 7 publications

(10 citation statements)

References 15 publications

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“…During the training process of the WSGAIN‐GP model, the input of the generator

G

is random noise tensor

{\rm Z} ( t )

and mask tensor

{\rm M} ( t )

that can simulate the distribution of missing data, and the output is imputed tensor

{\bar{\chi }}_{{\mathrm{oT}}}( t )

. The loss function of the generator is expressed as follows [26]:

\begin{eqnarray} {L}_G &=& \min \left[ {\left( {1 - {\mathrm{M}}\left( t \right)} \right) \odot D\left( {{{\bar{\chi }}}_{{\mathrm{oT}}}\left( t \right)} \right)} \right]\nonumber\\ &&+ \left[ {{\alpha }_G{\mathrm{M}}\left( t \right) \odot {{\left( {{{\hat{\chi }}}_{{\mathrm{oT}}}\left( t \right) - {{\bar{\chi }}}_{{\mathrm{oT}}}\left( t \right)} \right)}}^2} \right] \end{eqnarray}

where

{\alpha }_G

denotes the generator loss hyperparameter,

{\hat{\chi }}_{{\mathrm{oT}}}( t )

is the completed tensor obtained after integration that can be expressed as follows:

{\hat{χ}}_{oT}

…”

Section: Winding Insulation Degradation Evaluation Methods Based On I...mentioning

confidence: 99%

“…It incorporates GP technology to enhance algorithm stability. WSGAIN-GP also achieves lightweighting by reducing network depth, ensuring both quick calculation speed and accurate data completion [26,27]. This algorithm is particularly suitable for online degradation evaluation of winding insulation.…”

Section: Wsigain-gp Algorithmmentioning

confidence: 99%

“…During the training process of the WSGAIN-GP model, the input of the generator G is random noise tensor Z(t ) and mask tensor M(t ) that can simulate the distribution of missing data, and the output is imputed tensor χoT (t ). The loss function of the generator is expressed as follows [26]:…”

Section: Construction Of the Iot Sensing Data Completion Model Based ...mentioning

confidence: 99%

See 2 more Smart Citations

Evaluation method for insulation degradation of power transformer windings based on incomplete internet of things sensing data

Qu,

Zhao,

Zhao

et al. 2024

IET Science Measure & Tech

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This paper proposes a novel evaluation method to address the challenge of evaluating insulation degradation in power transformer windings based on incomplete online Internet of Things (IoT) sensing data. The method leverages the Wasserstein Slim Generative Adversarial Imputation Network with Gradient Penalty algorithm to fill the irregularly missing power transformer IoT perception data, including voltage, current, temperature, and partial discharge. Subsequently, electrical, thermal, and mechanical performance degradation damage indicators for transformer winding insulation are constructed using the filled and complete IoT perception data. By applying the tensor fusion algorithm, the characteristics of these degradation damage indicators are fused, leading to the development of a comprehensive degradation evaluation index for the winding insulation. The evaluation of the winding insulation degradation state is achieved through the minimum quantization error method. The proposed method is validated using the real‐world transformer IoT perception data, and the experimental results demonstrate its ability to accurately assess the degree of winding insulation degradation, regardless of the presence of random or continuous irregularities in IoT sensing data.

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“…During the training process of the WSGAIN‐GP model, the input of the generator

G

is random noise tensor

{\rm Z} ( t )

and mask tensor

{\rm M} ( t )

that can simulate the distribution of missing data, and the output is imputed tensor

{\bar{\chi }}_{{\mathrm{oT}}}( t )

. The loss function of the generator is expressed as follows [26]:

\begin{eqnarray} {L}_G &=& \min \left[ {\left( {1 - {\mathrm{M}}\left( t \right)} \right) \odot D\left( {{{\bar{\chi }}}_{{\mathrm{oT}}}\left( t \right)} \right)} \right]\nonumber\\ &&+ \left[ {{\alpha }_G{\mathrm{M}}\left( t \right) \odot {{\left( {{{\hat{\chi }}}_{{\mathrm{oT}}}\left( t \right) - {{\bar{\chi }}}_{{\mathrm{oT}}}\left( t \right)} \right)}}^2} \right] \end{eqnarray}

where

{\alpha }_G

denotes the generator loss hyperparameter,

{\hat{\chi }}_{{\mathrm{oT}}}( t )

is the completed tensor obtained after integration that can be expressed as follows:

{\hat{χ}}_{oT}

…”

Section: Winding Insulation Degradation Evaluation Methods Based On I...mentioning

confidence: 99%

Section: Wsigain-gp Algorithmmentioning

confidence: 99%

Section: Construction Of the Iot Sensing Data Completion Model Based ...mentioning

confidence: 99%

See 1 more Smart Citation

Evaluation method for insulation degradation of power transformer windings based on incomplete internet of things sensing data

Qu,

Zhao,

Zhao

et al. 2024

IET Science Measure & Tech

View full text Add to dashboard Cite

show abstract

“…CollaGAN (Lee et al, 2019) proposes a collaborative GAN for missing data imputation but it focuses on image data. WGAIN (Friedjungová et al, 2020), CGAIN (Awan et al, 2021), PC-GAIN (Wang et al, 2021) and S-GAIN (Neves et al, 2021) extend GAIN in various ways. IFGAN (Qiu et al, 2020) conducts missing data imputation using a feature-specific GAN and MCFlow (Richardson et al, 2020) proposes a Monte Carlo flow method for data imputation but no theoretical result is provided.…”

Section: Related Workmentioning

confidence: 99%

FragmGAN: Generative Adversarial Nets for Fragmentary Data Imputation and Prediction

Fang¹,

Shenliao²

2022

Preprint

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Modern scientific research and applications very often encounter "fragmentary data" which brings big challenges to imputation and prediction. By leveraging the structure of response patterns, we propose a unified and flexible framework based on Generative Adversarial Nets (GAN) to deal with fragmentary data imputation and label prediction at the same time. Unlike most of the other generative model based imputation methods that either have no theoretical guarantee or only consider Missing Completed At Random (MCAR), the proposed FragmGAN has theoretical guarantees for imputation with data Missing At Random (MAR) while no hint mechanism is needed. FragmGAN trains a predictor with the generator and discriminator simultaneously. This linkage mechanism shows significant advantages for predictive performances in extensive experiments.

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“…Furthermore, to increase the diversity of generated data, Gradient Penalty was introduced into WGAN, forming Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) [ 54 , 55 , 56 , 57 ]. Based on the GAN and WGAN architectures, dedicated generative imputation networks were developed for data imputation, namely, the Generative Adversarial Imputation Network (GAIN) [ 58 ] and Wasserstein Generative Adversarial Imputation Network (WGAIN) [ 59 ]. The Slim Generative Adversarial Imputation Network (SGAIN), a lightweight generative imputation network architecture without a hint matrix, was proposed as an improvement on the GAIN.…”

Section: Introductionmentioning

confidence: 99%

Enhancement Methods of Hydropower Unit Monitoring Data Quality Based on the Hierarchical Density-Based Spatial Clustering of Applications with a Noise–Wasserstein Slim Generative Adversarial Imputation Network with a Gradient Penalty

Zhang,

Guo,

Yuan

et al. 2023

Sensors

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In order to solve low-quality problems such as data anomalies and missing data in the condition monitoring data of hydropower units, this paper proposes a monitoring data quality enhancement method based on HDBSCAN-WSGAIN-GP, which improves the quality and usability of the condition monitoring data of hydropower units by combining the advantages of density clustering and a generative adversarial network. First, the monitoring data are grouped according to the density level by the HDBSCAN clustering method in combination with the working conditions, and the anomalies in this dataset are detected, recognized adaptively and cleaned. Further combining the superiority of the WSGAIN-GP model in data filling, the missing values in the cleaned data are automatically generated by the unsupervised learning of the features and the distribution of real monitoring data. The validation analysis is carried out by the online monitoring dataset of the actual operating units, and the comparison experiments show that the clustering contour coefficient (SCI) of the HDBSCAN-based anomaly detection model reaches 0.4935, which is higher than that of the other comparative models, indicating that the proposed model has superiority in distinguishing between the valid samples and anomalous samples. The probability density distribution of the data filling model based on WSGAIN-GP is similar to that of the measured data, and the KL dispersion, JS dispersion and Hellinger’s distance of the distribution between the filled data and the original data are close to 0. Compared with the filling methods such as SGAIN, GAIN, KNN, etc., the effect of data filling with different missing rates is verified, and the RMSE error of data filling with WSGAIN-GP is lower than that of other comparative models. The WSGAIN-GP method has the lowest RMSE error under different missing rates, which proves that the proposed filling model has good accuracy and generalization, and the research results in this paper provide a high-quality data basis for the subsequent trend prediction and state warning.

show abstract

SGAIN, WSGAIN-CP and WSGAIN-GP: Novel GAN Methods for Missing Data Imputation

Cited by 7 publications

References 15 publications

Evaluation method for insulation degradation of power transformer windings based on incomplete internet of things sensing data

Evaluation method for insulation degradation of power transformer windings based on incomplete internet of things sensing data

FragmGAN: Generative Adversarial Nets for Fragmentary Data Imputation and Prediction

Enhancement Methods of Hydropower Unit Monitoring Data Quality Based on the Hierarchical Density-Based Spatial Clustering of Applications with a Noise–Wasserstein Slim Generative Adversarial Imputation Network with a Gradient Penalty

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