A Reconstruction Method for Missing Data in Power System Measurement Based on LSGAN

Wang, Changgang; Cao, Yu; Shi, Z.; Ling, Tong

doi:10.3389/fenrg.2021.651807

Cited by 15 publications

(5 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, when CNN models extract features of occluded faces, the obstructed parts are embedded in the latent space representation [22]. (2) Algorithms based on generative adversarial networks [23]: GAN [24], WGAN [25], WGAN-GP [26], LSGAN [27], DCGAN [28], etc. These models generate clearer and more realistic samples, but suffer from poor training stability, gradient vanishing, and mode collapse issues.…”

Section: Related Workmentioning

confidence: 99%

WITHDRAWN: Masked face recognition with BF-FaceNet and multi-view features

Sun

et al. 2023

Preprint

View full text Add to dashboard Cite

Wearing masks in accordance with scientific guidelines is the most economically-effective protective measure for preventing respiratory diseases, such as COVID-19 and influenza, by interrupting viral transmission and safeguarding one's own health. In fact, researchers found that wearing masks can effectively reduce the infection rate of COVID-19 by 65%. However, this brings great inconvenience to face recognition, and human face recognition accuracy under occlusion conditions is low. Therefore, this paper proposes masked face recognition with BF-FaceNet and multi-view features. Firstly, in order to extract fine-grained features of the face region, ResNet50 is replaced by BoTNet as the backbone network of FaceNet. Secondly, a non-masked map is generated to accurately locate the non-masked area. Meanwhile, the face attention augmentation model (FAAM) is designed to extract local face features of the non-masked map. Thirdly, by combining loss function Ltriplet with Lattention, joint loss function Lface is proposed to improve the accuracy of masked recognition. Finally, experimental results on the publicly-available masked face dataset, SMFRD, demonstrate a significant improvement in recognition accuracy using our proposed algorithm compared to other methods.

show abstract

Section: Related Workmentioning

confidence: 99%

WITHDRAWN: Masked face recognition with BF-FaceNet and multi-view features

Sun

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Therefore, how to accurately interpolate missing values in power grid dispatch data has become an urgent problem that needs to be solved. Reference [5] proposes a power system missing data reconstruction model that considers spatiotemporal characteristics, in response to the traditional method of missing data reconstruction in power systems that only considers data distribution patterns and neglects data temporal and spatial characteristics. Reference [6] proposes a method for completing missing power load data based on an improved bidirectional genetic BP neural network to address the issue of missing power load data.…”

Section: Introductionmentioning

confidence: 99%

A missing sequence interpolation algorithm for power grid dispatching data under multisource data fusion

Chen,

Zhang,

et al. 2024

Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023)

View full text Add to dashboard Cite

To solve the problem of missing values in dispatching data in power system, so as to improve the security and stability of power system. This article proposes a missing sequence interpolation algorithm for power grid dispatch data based on multi-source data fusion. Firstly, preprocess the multi-source power grid dispatch data, including data normalization, data serialization, etc. Then, construct an LSTM long-term and short-term memory network model, and train the model using existing data to learn the features and patterns in the original data. Next, tensor PARAFAC decomposition is implemented through first-order partial derivatives to interpolate sequence data containing missing values. Finally, the effectiveness of the evaluation algorithm is verified through experiments. The experimental results show that the algorithm proposed in this paper has high interpolation accuracy and real-time performance, and can effectively interpolate missing values in power grid dispatch data.

show abstract

“…Reference [14] proposed a bidirectional recurrent imputation time series (BRITS) method, leveraging bi-directional recurrent neural networks to capture the dynamic properties of time series data from both directions and provide accurate predictions for missing values. Reference [15] proposed a method using artificial neural networks to estimate missing synchrophasor data, predicting missing data values from existing complete data sets. Reference [16] proposed a method using an improved generative adversarial network, which can learn the distribution of measurement data in the power system and realize the reconstruction of missing measurement data with higher accuracy.…”

Section: Introductionmentioning

confidence: 99%

Bad Data Repair for New Energy Stations in Power System Based on Multi-Model Parallel Integration Approach

Li,

Liu,

Gao

et al. 2024

Electronics

View full text Add to dashboard Cite

The accurate and reliable acquisition of measurement information is very important for the stable operation of power systems, especially the operation status information of new energy stations. With the increasing proportion of new energy stations in power systems, the quality issues of data from these stations, caused by communication congestion, interference, and network attacks, become more pronounced. In this paper, to deal with the issue of low accuracy and poor performance of bad data restoration in new energy stations, a novel deep learning approach by combining the modified long short-term memory (LSTM) neural network and Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is proposed. The proposed method can be implemented in a parallel ensemble way. First, the normal data set acquired from multiple sections of new energy stations is utilized to train the modified LSTM and WGAN-GP model. Secondly, according to the data characteristics and rules captured by each model, the two models are systematically integrated and the bad data repair model pool is constructed. Subsequently, the results of model repair are screened and merged twice by the parallel integration framework to obtain the final repair result. Finally, the extensive experiments are carried out to verify the proposed method. The simulative results of energy stations in a real provincial power grid demonstrate that the proposed method can effectively repair bad data, thereby enhancing the data quality of new energy stations.

show abstract

A Reconstruction Method for Missing Data in Power System Measurement Based on LSGAN

Cited by 15 publications

References 20 publications

WITHDRAWN: Masked face recognition with BF-FaceNet and multi-view features

WITHDRAWN: Masked face recognition with BF-FaceNet and multi-view features

A missing sequence interpolation algorithm for power grid dispatching data under multisource data fusion

Bad Data Repair for New Energy Stations in Power System Based on Multi-Model Parallel Integration Approach

Contact Info

Product

Resources

About