Semi-ProtoPNet Deep Neural Network for the Classification of Defective Power Grid Distribution Structures

Stefenon, Stéfano Frizzo; Singh, Gurmail; Yow, Kin Choong; Cimatti, Alessandro

doi:10.3390/s22134859

Cited by 41 publications

(20 citation statements)

References 70 publications

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“…The use of artificial intelligence techniques for fault identification has been growing over the years, becoming nowadays a hot topic, especially for the electric power system [23]. 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].…”

Section: Related Workmentioning

confidence: 99%

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

Branco¹,

Cavalca²,

Stefenon³

et al. 2022

Preprint

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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.

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Section: Related Workmentioning

confidence: 99%

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

Branco¹,

Cavalca²,

Stefenon³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The accumulation of contamination and consequently PDs can cause the equipment to be at risk and lead to outages, which makes the monitoring of these insulators essential for electrical utility [50,51]. If maintenance is not done and the insulation fails, a technician should perform corrective maintenance, searching and replacing the insulator in the field in an emergency manner [52].…”

Section: Insulators Contaminationmentioning

confidence: 99%

Fault Prediction Based on Leakage Current in Contaminated Insulators Using Enhanced Time Series Forecasting Models

Neto¹,

Stefenon²,

Meyer³

et al. 2022

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To improve the monitoring of the electrical power grid it is necessary to evaluate the influence of contamination in relation to leakage current and its progression to a disruptive discharge. In this paper, insulators were tested in a saline chamber to simulate the increase of salt contamination on their surface, and to evaluate the supportability of these components. From the time series forecasting of the leakage current, it is possible to evaluate the development of the fault before a flashover occurs. Choosing which method to use is always a difficult task since some models may have higher computational effort. In this paper, for a complete evaluation, the long short-term memory (LSTM), group method of data handling (GMDH), adaptive neuro-fuzzy inference system (ANFIS), bootstrap aggregation (bagging), sequential learning (boosting), random subspace, and stacked generalization (stacking) ensemble learning models are analyzed. A review and comparison of these well-established methods for time series forecasting is performed. From the results of the best structure of the model, the hyperparameters are evaluated and the Wavelet transform is used to obtain an enhanced model.

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“…Such a task would be impossible for humans to perform manually. Some of these ML models are Echo state networks [50], ensemble learning methods [51][52][53], k-nearest neighbors (K-NN) [54], group method of data handling (GMDH) [55], long short-term memory (LSTM) [56], convolutional neural networks (CNNs) [57][58][59][60], and adaptive neuro-fuzzy inference system (ANFIS) [61].…”

Section: Assisted Technology Aiot and Machine Learningmentioning

confidence: 99%

Artificial Intelligence of Things Applied to Assistive Technology: A Systematic Literature Review

Freitas¹,

Piai²,

Farias³

et al. 2022

Preprint

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According to the World Health Organization, about 15% of the world’s population has some form of disability. Assistive Technology, in this context, contributes directly to the overcoming of difficulties encountered by people with disabilities in their daily lives, allowing them to receive education and become part of the labor market and society in a worthy manner. Assistive Technology has made great advances in its integration with Artificial Intelligence of Things (AIoT) devices. AIoT processes and analyzes the large amount of data generated by IoT devices and applies Artificial Intelligence models, specifically Machine Learning, to discover patterns for generating insights and assisting in decision making. Based on a systematic literature review, this article aims at identifying the Machine Learning models used in multiple different research about Artificial Intelligence of Things applied to Assistive Technology. The survey of the topics approached in this article also highlights the context of such research, their application, IoT devices used, and gaps and opportunities for further development. Survey results show that 50% of the analyzed research address visual impairment, and for this reason, most of the topics cover issues related to computational vision. Portable devices, wearables, and smartphones constituted the majority of IoT devices. Deep Neural Networks represent 81% of the Machine Learning models applied in the reviewed research.

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Semi-ProtoPNet Deep Neural Network for the Classification of Defective Power Grid Distribution Structures

Cited by 41 publications

References 70 publications

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

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

Fault Prediction Based on Leakage Current in Contaminated Insulators Using Enhanced Time Series Forecasting Models

Artificial Intelligence of Things Applied to Assistive Technology: A Systematic Literature Review

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