Variational Autoencoder Based Fault Detection and Location Method for Power Distribution Network

Wang, Xuhong; Cui, Ping; Du, Ying; Yang, Yupu

doi:10.1109/cmd48350.2020.9287286

Cited by 8 publications

(5 citation statements)

References 10 publications

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“…In terms of classification problems, the VAE outperforms the DBN as it is more data-efficient and robust to noise and uncertainties. For instance, the VAE yields an F-score of 83.39 on the IEEE 33-bus system which is 5.72% higher than the DBN in fault identification [158]. Similar improvements can be seen in attack recognition problems, which are classification challenges [141,170].…”

Section: Variational Autoencodermentioning

confidence: 65%

“…where D KL is the Kullback-Leibler divergence [158] between the encoder distribution and the prior distribution, p(z), which is usually assumed to be a unit Gaussian distribution. The first term in the ELBO is the reconstruction loss, which measures the similarity between the input data and the reconstructed data, while the second term is the regularization term, which encourages the latent representation to follow the prior distribution [64].…”

Section: Variational Autoencodermentioning

confidence: 99%

See 1 more Smart Citation

Deep Neural Networks in Power Systems: A Review

Khodayar

Regan

2023

Energies

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Identifying statistical trends for a wide range of practical power system applications, including sustainable energy forecasting, demand response, energy decomposition, and state estimation, is regarded as a significant task given the rapid expansion of power system measurements in terms of scale and complexity. In the last decade, deep learning has arisen as a new kind of artificial intelligence technique that expresses power grid datasets via an extensive hypothesis space, resulting in an outstanding performance in comparison with the majority of recent algorithms. This paper investigates the theoretical benefits of deep data representation in the study of power networks. We examine deep learning techniques described and deployed in a variety of supervised, unsupervised, and reinforcement learning scenarios. We explore different scenarios in which discriminative deep frameworks, such as Stacked Autoencoder networks and Convolution Networks, and generative deep architectures, including Deep Belief Networks and Variational Autoencoders, solve problems. This study’s empirical and theoretical evaluation of deep learning encourages long-term studies on improving this modern category of methods to accomplish substantial advancements in the future of electrical systems.

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Section: Variational Autoencodermentioning

confidence: 65%

Section: Variational Autoencodermentioning

confidence: 99%

Deep Neural Networks in Power Systems: A Review

Khodayar

Regan

2023

Energies

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“…VAE combined with neural layers and its versions were explained for the uses of anomaly detection. Also, the process of forecasting became more efficient [53][54][55][56].…”

Section: V) Variational Auto-encoders (Vae)mentioning

confidence: 99%

Load Forecasting Techniques for Power System: Research Challenges and Survey

et al. 2022

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The main and pivot part of electric companies is the load forecasting. Decision-makers and think tank of power sectors should forecast the future need of electricity with large accuracy and small error to give uninterrupted and free of load shedding power to consumers. The demand of electricity can be forecasted amicably by many Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI) techniques among which hybrid methods are most popular. The present technologies of load forecasting and present work regarding combination of various ML, DL and AI algorithms are reviewed in this paper. The comprehensive review of single and hybrid forecasting models with functions; advantages and disadvantages are discussed in this paper. The comparison between the performance of the models in terms of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) values are compared and discussed with literature of different models to support the researchers to select the best model for load prediction. This comparison validates the fact that the hybrid forecasting models will provide a more optimal solution.INDEX TERMS load forecasting, machine learning, load shedding, root mean squared error, mean absolute percentage error.

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“…VAEs are relatively a new class of AEs in energy systems which employ the concept of variational inference and Bayesian optimization to carry out the encoding and decoding operations [126, 127]. VAE integrated with recurrent neural layers and its variants have been recently explored for the application of anomaly detection and to make the forecasting process more efficient [128–131]. To reduce the dimensionality arising from time lags considered in renewable generation forecasting, VAEs have been implemented in [132] before performing forecasts with BLSTM.…”

Section: Data Pre‐processing Techniques For Energy Forecastingmentioning

confidence: 99%

“…tion of anomaly detection and to make the forecasting process more efficient [128][129][130][131]. To reduce the dimensionality arising from time lags considered in renewable generation forecasting, VAEs have been implemented in [132] before performing forecasts with BLSTM.…”

Section: Variational Auto-encoders (Vae)mentioning

confidence: 99%

Energy forecasting in smart grid systems: recent advancements in probabilistic deep learning

Kaur

Islam

Mahmud

et al. 2022

IET Generation Trans & Dist

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Energy forecasting plays a vital role in mitigating challenges in data rich smart grid (SG) systems involving various applications such as demand-side management, load shedding, and optimum dispatch. Managing efficient forecasting while ensuring the least possible prediction error is one of the main challenges posed in the grid today, considering the uncertainty in SG data. This paper presents a comprehensive and application-oriented review of state-of-the-art forecasting methods for SG systems along with recent developments in probabilistic deep learning (PDL). Traditional point forecasting methods including statistical, machine learning (ML), and deep learning (DL) are extensively investigated in terms of their applicability to energy forecasting. In addition, the significance of hybrid and data preprocessing techniques to support forecasting performance is also studied. A comparative case study using the Victorian electricity consumption in Australia and American electric power (AEP) datasets is conducted to analyze the performance of deterministic and probabilistic forecasting methods. The analysis demonstrates higher efficacy of DL methods with appropriate hyper-parameter tuning when sample sizes are larger and involve nonlinear patterns. Furthermore, PDL methods are found to achieve at least 60% lower prediction errors in comparison to other benchmark DL methods. However, the execution time increases significantly for PDL methods due to large sample space and a tradeoff between computational performance and forecasting accuracy needs to be maintained.

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Variational Autoencoder Based Fault Detection and Location Method for Power Distribution Network

Cited by 8 publications

References 10 publications

Deep Neural Networks in Power Systems: A Review

Deep Neural Networks in Power Systems: A Review

Load Forecasting Techniques for Power System: Research Challenges and Survey

Energy forecasting in smart grid systems: recent advancements in probabilistic deep learning

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