基于鸡群优化BP神经网络的变压器油纸绝缘老化预测方法
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国家自然科学基金(61533012,61673268)


Prediction of transformer oilpaper insulation aging based on BP neural networks with the chicken swarm optimization algorithm
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    摘要:

    为了深入研究变压器油纸绝缘老化状态与极化/去极化电流的内在联系,提出一种基于鸡群优化BP神经网络的变压器中油纸绝缘系统老化程度的预测方法。首先研究聚合度与扩展Debye模型的参数之间的关系。针对环境温度改变时,极化/去极化电流发生变化导致扩展Debye模型参数不能正确地反应油纸绝缘的老化状态的问题,训练BP神经网络拟合去极化电流和油纸聚合度间的关系,以消除环境温度变化带来的误差,实现不同温度下的油纸绝缘老化预测。然后针对BP神经网络收敛速度慢、预测效率低问题,采用鸡群算法优化BP神经网络的权值和阈值。此方法不仅加快网络的收敛速度,而且有效避免了算法寻优时易陷入局部最优解的现象。最后,针对此方法进行仿真分析。仿真结果证明,此方法能够校正环境温度误差对极化/去极化电流的影响,实现油纸聚合度的预测,具有较高的准确性。

    Abstract:

    In order to study the relationship between the aging and the polarization/depolarization current (PDC) of transformer oilpaper, a prediction method of transformer oilpaper aging is presented based on the BP neural network with the chicken swarm optimization algorithm. Firstly, the relationship between extended Debye parameters and the polymerization degree (DP) of oilpaper is examined. With the variation of atmosphere temperature, PDC changes and it leads to a failure of extended Debye model to response the aging status of oilpaper. In order to eliminate the error caused by temperature, a BP neural network is trained through fitting PDC and DP of oilpaper. Then, in view of the slow convergence and low efficiency of BP neural network, the chickens swarm algorithm is utilized to optimize weights and threshold of the BP neural network. After the optimization, the network convergence is speeded up and the possibility of trapping into local optimal is also reduced. Finally, the simulation results show that the environment influences to polarization/depolarization current are reduced and the oilpaper polymerization degree is predicted accurately.

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袁佳波,徐鹏程,李 磊,刘彦文,王 昕,郑益慧.基于鸡群优化BP神经网络的变压器油纸绝缘老化预测方法[J].电力科学与技术学报,2020,35(4):33-41. YUAN Jiabo, XU Pengcheng, LI Lei, LIU Yanwen, WANG Xin, ZHENG Yihui. Prediction of transformer oilpaper insulation aging based on BP neural networks with the chicken swarm optimization algorithm[J]. JOURNAL OF EIECTRIC POWER SCIENCE AND TECHNOLOGY,2020,35(4):33-41.

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  • 在线发布日期: 2020-09-04