Deep Learning in High Voltage Engineering: A Literature Review

Mantach, Sara; Lutfi, Abdulla; Tavasani, Hamed Moradi; Ashraf, Ahmed Bilal; El-Hag, Ayman H.; Kordi, Behzad

doi:10.3390/en15145005

Cited by 24 publications

(12 citation statements)

References 131 publications

(123 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning models have been increasingly used to improve the ability to identify faults in an electrical grid [ 24 , 25 , 26 ]. However, as these models have a large number of layers, they require more computational effort, making the choice of the appropriate model a challenge [ 27 ].…”

Section: Related Workmentioning

confidence: 99%

Wavelet LSTM for Fault Forecasting in Electrical Power Grids

Branco

Cavalca

Stefenon

et al. 2022

Sensors

View full text Add to dashboard Cite

An electric power distribution utility is responsible for providing energy to consumers in a continuous and stable way. Failures in the electrical power system reduce the reliability indexes of the grid, directly harming its performance. For this reason, there is a need for failure prediction to reestablish power in the shortest possible time. Considering an evaluation of the number of failures over time, this paper proposes performing failure prediction during the first year of the pandemic in Brazil (2020) to verify the feasibility of using time series forecasting models for fault prediction. The long short-term memory (LSTM) model will be evaluated to obtain a forecast result that an electric power utility can use to organize maintenance teams. The wavelet transform has shown itself to be promising in improving the predictive ability of LSTM, making the wavelet LSTM model suitable for the study at hand. The assessments show that the proposed approach has better results regarding the error in prediction and has robustness when statistical analysis is performed.

show abstract

Section: Related Workmentioning

confidence: 99%

Wavelet LSTM for Fault Forecasting in Electrical Power Grids

Branco

Cavalca

Stefenon

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Deep learning models have being increasingly used to improve the ability to identify faults in the electrical grid [24][25][26]. However, as these models have a large number of layers, they require more computational effort, making the choice of the appropriate model a challenge [27]. From the image processing of failed components, it is possible to identify patterns and thus improve their identification in the field [28].…”

Section: Related Workmentioning

confidence: 99%

Wavelet Long Short-Term Memory to Fault Forecasting in Electrical Power Grids

Branco¹,

Cavalca²,

Stefenon³

et al. 2022

Preprint

View full text Add to dashboard Cite

The electric power distribution utility is responsible for providing energy to consumers in a continuous and stable way, failures in the electrical power system reduce the reliability indexes of the grid, directly harming its performance. For this reason, there is a need for failure prediction to reestablish power in the shortest possible time. Considering an evaluation of the number of failures over time, this paper proposes to perform a failure prediction during the first year of the pandemic in Brazil (2020) to verify the feasibility of using time series forecasting models for fault prediction. The Long Short-Term Memory (LSTM) model will be evaluated to obtain a forecast result that can be used by the electric power utility to organize the maintenance teams. The Wavelet transform shows to be promising in improving the predictive ability of the LSTM, making the Wavelet LSTM model suitable for the study at hand. The results show that the proposed approach has better results regarding the evaluation of the error in prediction and has robustness when a statistical analysis is performed.

show abstract

“…Deep learning provides a solution to this problem as it can use raw data, and feature selection is integrated within the learning process [ 67 ]. A comprehensive review of deep learning in HV applications is given in [ 68 ], which also highlights the shortcomings or future needs of deep learning applications. A key challenge is the lack of real-world measurement data, as this is the basis for the development of any AI/ML/DL algorithm.…”

Section: Applications Of Measurement Data Manipulationmentioning

confidence: 99%

Switchgear Digitalization—Research Path, Status, and Future Work

Kaštelan

Vujović

Krčum

et al. 2022

Sensors

View full text Add to dashboard Cite

To keep pace with global energy efficiency trends and, in particular, emission reduction targets in the maritime sector, both onshore and maritime power distribution systems need to be adapted to the relevant new technologies and concepts. As an important link in the distribution chain, medium-voltage switchgear (MV) is expected to be stable and reliable while operating as efficiently as possible. Failures of MV equipment, while rare because the equipment must be safe to handle and use, have far-reaching consequences. The consequences of such failures, whether to the shore or marine power system, present risks to the entire power plant, so an accurate assessment of equipment condition is required to identify potential failures early. The solution is an emerging concept of digital switchgear, where the implementation of sensor technology and communication protocols enables effective condition monitoring, and the creation of a database that, when combined with machine learning algorithms, enables predictive maintenance and/or fault detection. This paper presents, step by step, the previous challenges, the current research (divided into predictive maintenance, condition monitoring, and fault detection categories), and the future directions in this field. The use of artificial intelligence is discussed to eliminate the disadvantage of manually interpreting operational data, and recommendations for future work are formulated, such as the need to standardize test procedures and data sets to train and compare different algorithms before they are used in practice.

show abstract

Deep Learning in High Voltage Engineering: A Literature Review

Cited by 24 publications

References 131 publications

Wavelet LSTM for Fault Forecasting in Electrical Power Grids

Wavelet LSTM for Fault Forecasting in Electrical Power Grids

Wavelet Long Short-Term Memory to Fault Forecasting in Electrical Power Grids

Switchgear Digitalization—Research Path, Status, and Future Work

Contact Info

Product

Resources

About