Equilibrio de carga para transformadores de distribucion electrica mejorando la calidad de servicio en fin de linea
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916900990091264 |
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| author | Bordón, Juan M. Jimenez, Victor A. Will, Adrian |
| author_facet | Bordón, Juan M. Jimenez, Victor A. Will, Adrian |
| contents | The distribution of electrical energy faces global challenges, such as increasing demand, the integration of distributed generation, high energy losses, and the need to improve service quality. In particular, load imbalance-where loads are not evenly distributed across the circuit phase-can reduce efficiency, shorten equipment lifespan, and increase susceptibility to service interruptions. While methods that involve shifting loads from one phase to another can be costly, they are effective when smart meters are available and implemented efficiently. This work proposes the use of genetic algorithms to optimally identify which loads should be reassigned in order to improve both phase balance and voltage quality at the end nodes of the network while minimizing the number of required changes. The algorithm was evaluated through simulations using PandaPower, a power flow analysis tool, modeling simple networks based on real-world characteristics of the electrical system in Tucuman. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_09235 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Equilibrio de carga para transformadores de distribucion electrica mejorando la calidad de servicio en fin de linea Bordón, Juan M. Jimenez, Victor A. Will, Adrian Neural and Evolutionary Computing The distribution of electrical energy faces global challenges, such as increasing demand, the integration of distributed generation, high energy losses, and the need to improve service quality. In particular, load imbalance-where loads are not evenly distributed across the circuit phase-can reduce efficiency, shorten equipment lifespan, and increase susceptibility to service interruptions. While methods that involve shifting loads from one phase to another can be costly, they are effective when smart meters are available and implemented efficiently. This work proposes the use of genetic algorithms to optimally identify which loads should be reassigned in order to improve both phase balance and voltage quality at the end nodes of the network while minimizing the number of required changes. The algorithm was evaluated through simulations using PandaPower, a power flow analysis tool, modeling simple networks based on real-world characteristics of the electrical system in Tucuman. |
| title | Equilibrio de carga para transformadores de distribucion electrica mejorando la calidad de servicio en fin de linea |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2505.09235 |