摘要
采用自适应遗传算法 ( AGA)作为神经网络的学习算法 ,对实验室中变压器超高频局部放电自动识别系统检测到的 5种放电类型进行了模式识别。实验结果表明 ,AGA神经网络解决了 BP神经网络对初始权值敏感、收敛速度慢和容易局部收敛的问题 ,具有较高的识别率和较强的推广能力 。
Based on the study of discharge properties, an automatic pattern recognition system for transformer ultra-high-frequency (UHF) PD is designed and developed in this paper.An adaptive genetic algorithm (AGA) is used to train the neural network (NN) in system. Using BP-NN and AGA-NN, we distinguish the basic types of defects appearing in transformers, such as corona, void, bubble, creeping discharge and floating discharge. Tests in laboratory show that the results are satisfactory. Compared with BP-NN, AGA-NN can overcome slow convergence and possibility of being trapped at local minimum value. Thus, the convergence, discrimination and generalization ability of AGA-NN are more powerful.
出处
《湖南电力》
2004年第5期4-7,37,共5页
Hunan Electric Power
关键词
变压器
超高频局部放电检测
模式识别
自适应遗传算法
神经网络
transformer
ultra-high-frequency PD detection
pattern recognition
adaptive genetic algorithm
neural network
