DeVLearn: A Deep Visual Learning Framework for Determining the Location of Temporary Faults in Power Systems

Biswas, Shuchismita; Meyur, Rounak; Centeno, Virgilio

doi:10.1109/smartgridcomm47815.2020.9302969

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…It has been used in a variety of applications. For example, Biswas et al [139] use VAE in the context of detecting faults in transmission networks. Zheng and Gu [140] use the VAE for detecting anomalies in power system forecasting.…”

Section: And Cyberattacksmentioning

confidence: 99%

Data‐driven operation of the resilient electric grid: A case of COVID‐19

Noorazar

Srivastava

Pannala

et al. 2021

The Journal of Engineering

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Electrical energy is a vital part of modern life, and expectations for grid resilience to allow a continuous and reliable energy supply has tremendously increased even during adverse events (e.g. Ukraine cyberattack, Hurricane Maria). The global pandemic COVID‐19 has raised the electric energy reliability risk due to potential workforce disruptions, supply chain interruptions, and increased possible cybersecurity threats. Additionally, the pandemic introduces a significant degree of uncertainty to the grid operation in the presence of other challenges including aging power grids, high proliferation of distributed generation, market mechanism, and active distribution network. This situation increases the need for measures for the resiliency of power grids to mitigate the impact of the pandemic as well as simultaneous extreme events including cyberattacks and adverse weather events. Solutions to manage such an adverse scenario will be multi‐fold: (a) emergency planning and organisational support, (b) following safety protocol, (c) utilising enhanced automation and sensing for situational awareness, and (d) integration of advanced technologies and data points for ML‐driven enhanced decision support. Enhanced digitalisation and automation resulted in better network visibility at various levels, including generation, transmission, and distribution. These data or information can be employed to take advantage of advanced machine learning techniques for automation and increased power grid resilience. In this paper, the resilience of power grids in the face of pandemics is explored and various machine learning tools that can be helpful to augment human operators are discused by: (a) reviewing the impact of COVID‐19 on power grid operations and actions taken by operators/organisations to minimise the impact of COVID‐19, and (b) presenting recently developed tools and concepts of machine learning and artificial intelligence that can be applied to increase the resiliency of power systems in normal and extreme scenarios such as the COVID‐19 pandemic.

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Section: And Cyberattacksmentioning

confidence: 99%

Data‐driven operation of the resilient electric grid: A case of COVID‐19

Noorazar

Srivastava

Pannala

et al. 2021

The Journal of Engineering

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“…VAE is implemented in [30] to forecast the solar generation data. Furthermore, reconstructed VAEs integrated with deep learning have been widely utilized for fault and anomaly detection in time series energy data using reconstruction scores from the decoding process [31]- [33].…”

Section: Introductionmentioning

confidence: 99%

A VAE-Bayesian Deep Learning Scheme for Solar Generation Forecasting based on Dimensionality Reduction

Kaur¹,

Islam²,

Mahmud³

et al. 2021

Preprint

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The advancement in distributed generation technologies in modern power systems has led to a widespread integration of renewable power generation at customer side. However, the intermittent nature of renewable energy pose new challenges to the network operational planning with underlying uncertainties. This paper proposes a novel Bayesian probabilistic technique for forecasting renewable power generation by addressing data and model uncertainties by integrating bidirectional long short-term memory (BiLSTM) neural networks while compressing the weight parameters using variational autoencoder (VAE). Existing Bayesian deep learning methods suffer from high computational complexities as they require to draw a large number of samples from weight parameters expressed in the form of probability distributions. The proposed method can deal with uncertainty present in model and data in a more computationally efficient manner by reducing the dimensionality of model parameters. The proposed method is evaluated using pinball loss, reconstruction error, and other forecasting evaluation metrics. It is inferred from the numerical results that VAE-Bayesian BiLSTM outperforms other probabilistic deep learning methods in terms of forecasting accuracy and computational efficiency for different sizes of the dataset.

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