Loss reduction optimization strategies for medium and low-voltage distribution networks based on Intelligent optimization algorithms
Abstract Problem With the rapid development of social economy, the problem of line losses in distribution networks gradually becomes prominent, which directly affects the efficiency and economy of power systems. Methodology In order to reduce line losses, a loss optimization model for low and medium...
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SpringerOpen
2024-11-01
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| Series: | Energy Informatics |
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| Online Access: | https://doi.org/10.1186/s42162-024-00442-z |
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| author | Nian Liu Yuehan Zhao |
| author_facet | Nian Liu Yuehan Zhao |
| author_sort | Nian Liu |
| collection | DOAJ |
| description | Abstract Problem With the rapid development of social economy, the problem of line losses in distribution networks gradually becomes prominent, which directly affects the efficiency and economy of power systems. Methodology In order to reduce line losses, a loss optimization model for low and medium voltage distribution networks based on an improved Gray Wolf optimization support vector machine is proposed. The optimization model introduces a dimensional learning strategy based on the original model to enhance the adaptability and robustness of the model. Results The experimental results show that the Mean Absolute Percent Error (MAPE) of the proposed algorithm is 8.62%, the Mean Absolute Error (MAE) is 1.30% and the Root Mean Square Error (RMSE) is 2.26%. Compared with the traditional Gray Wolf Optimized Support Vector Machine, the errors of the improved model are reduced by 15.27%, 3.33% and 4.70%, respectively. Contributions Our study demonstrates that extracellular vesicles secreted by the gut microbiota can influence the nervous system via the microbial-gut-brain axis. Furthermore, we found that the extracellular vesicles secreted by the gut microbiota from the probiotic group exert a beneficial therapeutic effect on anxiety and hippocampal neuroinflammation. |
| format | Article |
| id | doaj-art-e55450564e414f1cb2dc29b1fbe7845f |
| institution | OA Journals |
| issn | 2520-8942 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Energy Informatics |
| spelling | doaj-art-e55450564e414f1cb2dc29b1fbe7845f2025-08-20T02:38:35ZengSpringerOpenEnergy Informatics2520-89422024-11-017111910.1186/s42162-024-00442-zLoss reduction optimization strategies for medium and low-voltage distribution networks based on Intelligent optimization algorithmsNian Liu0Yuehan Zhao1Strategy & Planning Department, China Southern Power Grid Co., LtdSouthern Power Grid Supply Chain Group Co., Ltd OfficeAbstract Problem With the rapid development of social economy, the problem of line losses in distribution networks gradually becomes prominent, which directly affects the efficiency and economy of power systems. Methodology In order to reduce line losses, a loss optimization model for low and medium voltage distribution networks based on an improved Gray Wolf optimization support vector machine is proposed. The optimization model introduces a dimensional learning strategy based on the original model to enhance the adaptability and robustness of the model. Results The experimental results show that the Mean Absolute Percent Error (MAPE) of the proposed algorithm is 8.62%, the Mean Absolute Error (MAE) is 1.30% and the Root Mean Square Error (RMSE) is 2.26%. Compared with the traditional Gray Wolf Optimized Support Vector Machine, the errors of the improved model are reduced by 15.27%, 3.33% and 4.70%, respectively. Contributions Our study demonstrates that extracellular vesicles secreted by the gut microbiota can influence the nervous system via the microbial-gut-brain axis. Furthermore, we found that the extracellular vesicles secreted by the gut microbiota from the probiotic group exert a beneficial therapeutic effect on anxiety and hippocampal neuroinflammation.https://doi.org/10.1186/s42162-024-00442-zGrey wolf algorithmDistribution networkLoss reduction optimizationRegression support vector |
| spellingShingle | Nian Liu Yuehan Zhao Loss reduction optimization strategies for medium and low-voltage distribution networks based on Intelligent optimization algorithms Energy Informatics Grey wolf algorithm Distribution network Loss reduction optimization Regression support vector |
| title | Loss reduction optimization strategies for medium and low-voltage distribution networks based on Intelligent optimization algorithms |
| title_full | Loss reduction optimization strategies for medium and low-voltage distribution networks based on Intelligent optimization algorithms |
| title_fullStr | Loss reduction optimization strategies for medium and low-voltage distribution networks based on Intelligent optimization algorithms |
| title_full_unstemmed | Loss reduction optimization strategies for medium and low-voltage distribution networks based on Intelligent optimization algorithms |
| title_short | Loss reduction optimization strategies for medium and low-voltage distribution networks based on Intelligent optimization algorithms |
| title_sort | loss reduction optimization strategies for medium and low voltage distribution networks based on intelligent optimization algorithms |
| topic | Grey wolf algorithm Distribution network Loss reduction optimization Regression support vector |
| url | https://doi.org/10.1186/s42162-024-00442-z |
| work_keys_str_mv | AT nianliu lossreductionoptimizationstrategiesformediumandlowvoltagedistributionnetworksbasedonintelligentoptimizationalgorithms AT yuehanzhao lossreductionoptimizationstrategiesformediumandlowvoltagedistributionnetworksbasedonintelligentoptimizationalgorithms |