Intelligent Prediction of Transformer Loss for Low Voltage Recovery in Distribution Network with Unbalanced Load
In order to solve the problem of low voltage caused by unbalanced load in the distribution network, a transformer loss intelligent prediction model under unbalanced load is proposed. Firstly, the mathematical model of a transformer with an unbalanced load is established. The zero-sequence impedance...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/11/4432 |
| _version_ | 1797597668246028288 |
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| author | Zikuo Dai Kejian Shi Yidong Zhu Xinyu Zhang Yanhong Luo |
| author_facet | Zikuo Dai Kejian Shi Yidong Zhu Xinyu Zhang Yanhong Luo |
| author_sort | Zikuo Dai |
| collection | DOAJ |
| description | In order to solve the problem of low voltage caused by unbalanced load in the distribution network, a transformer loss intelligent prediction model under unbalanced load is proposed. Firstly, the mathematical model of a transformer with an unbalanced load is established. The zero-sequence impedance and neutral line current of the transformer are calculated by using the Chaos Game Optimization algorithm (CGO), and the correctness of the mathematical model is proved by using actual data. Then, the correlation among network input variables is eliminated by using Principal Component Analysis (PCA), so the number of network input variables is decreased. At the same time, Sparrow Search Algorithm (SSA) is used to optimize the initial weight and threshold of the BP network, and an accurate transformer loss prediction model based on the PCA-SSA-BP is established. Finally, compared with the transformer loss prediction model based on BP network, Genetic Algorithm optimized BP network (GA-BP), Particle Swarm optimized BP network (PSO-BP) and Sparrow Search Algorithm optimized BP network (SSA-BP), the transformer loss prediction model based on PCA-SSA-BP network has been proven to be accurate by using actual data and it is helpful for low-voltage recovery in the distribution network. |
| first_indexed | 2024-03-11T03:07:50Z |
| format | Article |
| id | doaj.art-a202cdd6ac334aef83c49c59bbdd7263 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T03:07:50Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-a202cdd6ac334aef83c49c59bbdd72632023-11-18T07:48:46ZengMDPI AGEnergies1996-10732023-05-011611443210.3390/en16114432Intelligent Prediction of Transformer Loss for Low Voltage Recovery in Distribution Network with Unbalanced LoadZikuo Dai0Kejian Shi1Yidong Zhu2Xinyu Zhang3Yanhong Luo4Equipment Management Department, State Grid Liaoning Electric Power Company, Shenyang 110055, ChinaElectric Power Research Institute, State Grid Liaoning Electric Power Company, Shenyang 110055, ChinaElectric Power Research Institute, State Grid Liaoning Electric Power Company, Shenyang 110055, ChinaElectric Power Research Institute, State Grid Liaoning Electric Power Company, Shenyang 110055, ChinaSchool of Information Science and Engineering, Northeastern University, Shenyang 110819, ChinaIn order to solve the problem of low voltage caused by unbalanced load in the distribution network, a transformer loss intelligent prediction model under unbalanced load is proposed. Firstly, the mathematical model of a transformer with an unbalanced load is established. The zero-sequence impedance and neutral line current of the transformer are calculated by using the Chaos Game Optimization algorithm (CGO), and the correctness of the mathematical model is proved by using actual data. Then, the correlation among network input variables is eliminated by using Principal Component Analysis (PCA), so the number of network input variables is decreased. At the same time, Sparrow Search Algorithm (SSA) is used to optimize the initial weight and threshold of the BP network, and an accurate transformer loss prediction model based on the PCA-SSA-BP is established. Finally, compared with the transformer loss prediction model based on BP network, Genetic Algorithm optimized BP network (GA-BP), Particle Swarm optimized BP network (PSO-BP) and Sparrow Search Algorithm optimized BP network (SSA-BP), the transformer loss prediction model based on PCA-SSA-BP network has been proven to be accurate by using actual data and it is helpful for low-voltage recovery in the distribution network.https://www.mdpi.com/1996-1073/16/11/4432unbalanced loadtransformer lossPCA-SSA-BP networkintelligent predictiondistribution network |
| spellingShingle | Zikuo Dai Kejian Shi Yidong Zhu Xinyu Zhang Yanhong Luo Intelligent Prediction of Transformer Loss for Low Voltage Recovery in Distribution Network with Unbalanced Load Energies unbalanced load transformer loss PCA-SSA-BP network intelligent prediction distribution network |
| title | Intelligent Prediction of Transformer Loss for Low Voltage Recovery in Distribution Network with Unbalanced Load |
| title_full | Intelligent Prediction of Transformer Loss for Low Voltage Recovery in Distribution Network with Unbalanced Load |
| title_fullStr | Intelligent Prediction of Transformer Loss for Low Voltage Recovery in Distribution Network with Unbalanced Load |
| title_full_unstemmed | Intelligent Prediction of Transformer Loss for Low Voltage Recovery in Distribution Network with Unbalanced Load |
| title_short | Intelligent Prediction of Transformer Loss for Low Voltage Recovery in Distribution Network with Unbalanced Load |
| title_sort | intelligent prediction of transformer loss for low voltage recovery in distribution network with unbalanced load |
| topic | unbalanced load transformer loss PCA-SSA-BP network intelligent prediction distribution network |
| url | https://www.mdpi.com/1996-1073/16/11/4432 |
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