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(广东电网有限责任公司清远供电局 变电管理所)
Application of probabilistic neural network with fruit fly optimization algorithm in power transformer fault diagnosis
ZHU Peiheng
(Substation Management,Qingyuan Power Supply Bureau of Guangdong Power Grid Co.,Ltd.)
本文已被:浏览 388次   下载 437
投稿时间:2018-04-14    修订日期:2018-05-09
中文摘要: 传统的变压器故障诊断方法存在编码不全,容易错判漏判的缺点。随着变压器在线监测技术的发展和产品需求的增加,变压器故障诊断技术朝着智能化的方向发展。为提高故障诊断率,结合油中气体分析法,本文提出了一种基于果蝇算法优化的概率神经网络模型的变压器故障诊断方法。作为一种新型的启发式和进化式算法,果蝇优化算法具有易理解和快速收敛到全局最优解的优点。概率神经网络结构简单、训练简洁,具有强大的非线性分类能力,将样本空间映射到故障模式空间中,从而形成一有较强容错能力和机构自适应能力的诊断网络。采用果蝇算法对模型参数进行优化,减少人为因素对神经网络设计的影响。仿真实验证明这种基于果蝇优化算法的概率神经网络可以有效地运用到变压器故障诊断中,为变压器故障诊断供了一条新途径,具有良好的研究价值和发展前景。
Abstract:The traditional transformer fault diagnosis method has incomplete coding, and it is easy to misjudge the fault. With the development of transformer on-line monitoring technology and the increase of product demand, which transformer fault diagnosis technology is developing in the direction of intelligence. In order to improve the accuracy of transformer fault diagnosis, combined with the gas analysis method in oil, a transformer fault diagnosis method based on fruit fly optimization algorithm (FOA) and probabilistic neural network (PNN) model is proposed. As a new heuristic and evolutionary algorithm, the fruit fly optimization algorithm has the advantages of easy understanding and fast convergence to the global optimal solution. The structure of the PNN is simple and the training is concise. PNN has strong ability of nonlinear classification, which the sample space is mapped to the fault pattern space to form a diagnostic network with strong fault tolerance and mechanism self-adaptive ability. The approach of FOA is used to optimize the model parameters to reduce the impact of human factors on the neural network design. The simulation experiments show that FOA-PNN can be effectively applied to transformer fault diagnosis and provides a new way for transformer fault diagnosis. The fault diagnosis method of FOA-PNN has a good research value and development prospects.
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