摘要
针对传统人工巡检和定期检测效率低、易受人为因素影响等不足。该文提出基于智能算法的换流变压器智能监测技术总体方案。首先,详细分析换流变压器声音信号产生机理,包括正常运行声音信号和故障状态声音信号。其次,阐述换流变压器声纹采集与故障状态声纹组成。再次,提出基于卷积神经网络的换流变压器智能检测算法。最后,通过实验结果分析验证该算法的可行性和有效性。结果表明,该智能算法能够有效地提高监测精度和预警效率,降低误报率,从而提高电力系统的安全稳定性。
Aiming at the shortcomings of traditional manual inspections and regular inspections,such as low efficiency and vulnerability to human factors,this paper proposes an overall plan for intelligent monitoring technology for converter transformers based on intelligent algorithms.First,the generation mechanism of converter transformer sound signals is analyzed in detail,including normal operation sound signals and fault state sound signals.Secondly,the voiceprint collection of converter transformers and the composition of voiceprint under fault conditions are described.Then,an intelligent converter transformer detection algorithm based on convolutional neural networks(CNNs)is proposed.Finally,the feasibility and effectiveness of the algorithm are verified through experimental results.The results show that the intelligent algorithm can effectively improve monitoring accuracy and early warning efficiency,reduce false alarm rate,and improve the security and stability of the power system.
出处
《科技创新与应用》
2025年第20期142-145,共4页
Technology Innovation and Application
关键词
智能算法
换流变压器
智能监测
故障状态声音
卷积神经网络
intelligent algorithm
converter transformer
intelligent monitoring
fault state sound
convolutional neural network
作者简介
第一作者:刘鹏华(1993-),男,硕士,工程师。研究方向为特高压换流变。
