Intelligent Prediction of Transformer Loss for Low Voltage Recovery in Distribution Network with Unbalanced Load

Zikuo Dai, Kejian Shi, Yidong Zhu, Xinyu Zhang and Yanhong Luo ()
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Zikuo Dai: Equipment Management Department, State Grid Liaoning Electric Power Company, Shenyang 110055, China
Kejian Shi: Electric Power Research Institute, State Grid Liaoning Electric Power Company, Shenyang 110055, China
Yidong Zhu: Electric Power Research Institute, State Grid Liaoning Electric Power Company, Shenyang 110055, China
Xinyu Zhang: Electric Power Research Institute, State Grid Liaoning Electric Power Company, Shenyang 110055, China
Yanhong Luo: School of Information Science and Engineering, Northeastern University, Shenyang 110819, China

Energies, 2023, vol. 16, issue 11, 1-19

Abstract: 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.

Keywords: unbalanced load; transformer loss; PCA-SSA-BP network; intelligent prediction; distribution network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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