基于粒子群优化-支持向量回归的变压器绕组温度软测量模型

doi:10.19595/j.cnki.1000-6753.tces.161792

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Abstract This paper proposes a new method of soft-measurement model for transformer winding hot spot temperature based on particle swarm optimization-support vector regression (PSO-SVR) algorithm, and verifies its prediction effect by making a better use of the monitoring information of the transformer. In this method, taken use of an improved particle swarm optimization algorithm based on passive aggregation, the SVR model parameters of the regression of support vector machine are optimized and the optimal solution is found. The relevant factors of the transformer operation are fully considered in predicting the temperature of the winding hot spots. Compared with BP neural network and SVR method, the training and prediction results of 110kV transformer in a sample city show that the model has better prediction ability.

Key words： Transformer winding hot spot temperature particular swarm optimization (PSO) support vector regression (SVR)

Received: 14 November 2016 Published: 24 April 2018

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TM411

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	Articles by authors
	Peng Daogang
	Chen Yuewei
	Qian Yuliang
	Huang Chao

Cite this article:

Peng Daogang,Chen Yuewei,Qian Yuliang等. Transformer Winding Temperature Soft Measurement Model Based on Particle Swarm Optimization-Support Vector Regression[J]. Transactions of China Electrotechnical Society, 2018, 33(8): 1742-1749.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.161792 OR https://dgjsxb.ces-transaction.com/EN/Y2018/V33/I8/1742

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