Location of faults based on deep learning with feature selection for meter placement in distribution power grids
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Iván L. Degano
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Leandro Fiaschetti
Abstract
A problem of great interest for power distribution companies is ensuring uninterrupted service in extensive power distribution systems. Thus, the monitoring of networks and identification of system faults become essential. This work focuses on identifying a fault’s occurrence from a small number of low-cost measurements in a power distribution system. The determination of sensor locations is based on the recent feature selection approach LassoNet, where the measurement locations are ranked. It provides the most informative measures during a fault resulting in a shortening data set. It is used as input to a deep neural network without a significant loss in accuracy. We validate our method on the IEEE 13 and 34 node test feeders for distribution systems to conduct the suggested approach’s experimental studies.
Funding source: Consejo Nacional de Investigaciones Científicas y Técnicas
Award Identifier / Grant number: PIP-11220200100815CO
Funding source: Agencia Nacional de Promoción Científica y Tecnológica
Award Identifier / Grant number: PICT-2019-02886
Acknowledgement
The authors thank Ismael Lemhadri and Louis Abraham for providing us with the code for LassoNet with the group lasso penalization.
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Research ethics: The authors assure that for this manuscript the following is fulfilled: 1. This material is our own original work, which has not been previously published elsewhere. 2. The paper is not currently being considered for publication elsewhere. 3. The paper reflects our own research and analysis in a truthful and complete manner. 4. The paper properly credits the meaningful contributions of co-authors and co-researchers. 5. The results are appropriately placed in the context of prior and existing research. 6. All sources used are properly cited. 7. All authors have been personally and actively involved in substantial work leading to the paper, and will take public responsibility for its content.
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Author contributions: Iván Degano: Conceived and designed the analysis, Contributed data or analysis tools, Performed the analysis, Wrote the paper; Leandro Fiaschetti: Collected the data, Contributed data or analysis tools, Performed the analysis, Wrote the paper; Pablo Lotito: Conceived and designed the analysis, Contributed data or analysis tools, Performed the analysis, Wrote the paper.
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Competing interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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Research funding: This research is supported by an ANPCYT (Argentina) grant (PICT-2019-02886). Furthermore, the investigation of I. Degano and P. Lotito are also supported by a CONICET (Argentina) grant (PIP-11220200100815CO).
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Data availability: The data supporting this study’s findings are available from the corresponding author, upon reasonable request.
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Articles in the same Issue
- Frontmatter
- Special Issue: Energy Storage and Management System for Electric Vehicles; Guest Editors: Achyut Shankar and Zahid Akhtar
- Optimization model of battery electric vehicle charging facility layout towards embedded system and data mining algorithm
- Investigation on voltage stability evaluation indicators and algorithms for power systems based on neural network algorithms
- Collaborative optimization algorithm for electric vehicle industry chain based on regional economic development needs
- Distributed generation aggregators considering low-carbon credits optimize dispatch strategies
- CFD simulation analysis optimization and experimental verification of heat dissipation problem of electric vehicle motor controller
- Logistics distribution route optimization of electric vehicles based on distributed intelligent system
- Analysis of green energy regeneration system for Electric Vehicles and Re estimation of carbon emissions in international trade based on evolutionary algorithms
- Regular Articles
- Location of faults based on deep learning with feature selection for meter placement in distribution power grids
- A novel computational technique to analyse the corona generated ionized field environment of EHV/UHV DC transmission lines
- Improving protection of compensated transmission line using IoT enabled adaptive auto reclosing scheme
- A new optimized ZV-ZCS non-isolated high-gain boost converter for renewable energy systems
- Performance of PMU in an electric distribution grid during transients
