<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This study explores an advanced method for improving line loss in station areas, a vital issue in the energy sector. By analyzing line loss data from the X-area station using data mining and a novel convolutional neural network (CNN) model optimized with particle swarm optimization, we aimed to pinpoint and diagnose line loss issues effectively. Our model, which integrates specific line loss predictive parameters, underwent rigorous training and testing with collected data. The results were promising: the model's predictions closely matched actual data, with most errors under 0.1. It outperformed existing models in iteration speed, convergence time, and accuracy, evidenced by lower mean square error (0.0112), root mean square error (0.1023), and average absolute error (3.2514%). This research presents a potent tool for distribution network analysis, offering practical insights for line loss localization and diagnosis.</p>
</abstract>

This study explores an advanced method for improving line loss in station areas, a vital issue in the energy sector. By analyzing line loss data from the X-area station using data mining and a novel convolutional neural network (CNN) model optimized with particle swarm optimization, we aimed to pinpoint and diagnose line loss issues effectively. Our model, which integrates specific line loss predictive parameters, underwent rigorous training and testing with collected data. The results were promising: the model's predictions closely matched actual data, with most errors under 0.1. It outperformed existing models in iteration speed, convergence time, and accuracy, evidenced by lower mean square error (0.0112), root mean square error (0.1023), and average absolute error (3.2514%). This research presents a potent tool for distribution network analysis, offering practical insights for line loss localization and diagnosis.

Line loss localization diagnosis and management measures of station area combined with data mining

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This study explores an advanced method for improving line loss in station areas, a vital issue in the energy sector. By analyzing line loss data from the...

Line loss localization diagnosis and management measures of station area combined with data mining

Data publikacji: 03 maj 2024

Zakres stron: -

Otrzymano: 10 kwi 2024

Przyjęty: 16 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-0943

Słowa kluczowe
Particle swarm algorithm, Convolutional neural network, Station area line loss prediction, Localization diagnosis, Governance measures

© 2024 Hui Cheng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Line loss localization diagnosis and management measures of station area combined with data mining

Data publikacji: 03 maj 2024

Zakres stron: -

Otrzymano: 10 kwi 2024

Przyjęty: 16 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-0943

Słowa kluczoweParticle swarm algorithm, Convolutional neural network, Station area line loss prediction, Localization diagnosis, Governance measures

© 2024 Hui Cheng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Particle swarm algorithm, Convolutional neural network, Station area line loss prediction, Localization diagnosis, Governance measures