摘 要
局部放电是电气设备绝缘劣化的重要诱因之一，其检测与定位对于保障电力系统安全运行具有重要意义。传统局部放电检测方法主要依赖于电气信号或单一物理量的采集，存在抗干扰能力弱、定位精度低等问题。为克服上述不足，本文提出了一种基于声学成像技术的局部放电检测新方法。该方法通过构建高灵敏度的麦克风阵列，捕捉局部放电产生的超声波信号，并结合波束形成算法实现放电源的空间定位。同时，引入深度学习模型对采集信号进行特征提取与模式识别，显著提升了检测的准确性和鲁棒性。实验结果表明，该方法能够在复杂电磁环境下有效区分背景噪声与局部放电信号，定位误差小于5厘米，且适用于多种类型电气设备的在线监测。研究创新性地将声学成像技术应用于局部放电检测领域，为电力设备状态评估提供了新的技术手段，具有重要的理论价值和工程应用前景。关键词：局部放电检测；声学成像技术；波束形成算法；深度学习；空间定位
Abstract
Partial discharge is one of the critical factors leading to insulation degradation in electrical equipment, and its detection and localization are of great significance for ensuring the safe operation of power systems. Traditional partial discharge detection methods mainly rely on electrical signals or the acquisition of single physical quantities, which suffer from weak interference resistance and low localization accuracy. To overcome these limitations, this study proposes a novel partial discharge detection method based on acoustic imaging technology. By constructing a highly sensitive microphone array, this method captures ultrasonic signals generated by partial discharges and combines them with beamforming algorithms to achieve spatial localization of the discharge source. Simultaneously, a deep learning model is introduced for feature extraction and pattern recognition of the collected signals, significantly enhancing the accuracy and robustness of the detection process. Experimental results demonstrate that this method can effectively distinguish between background noise and partial discharge signals in complex electromagnetic environments, with a localization error of less than 5 centimeters. It is also applicable for online monitoring of various types of electrical equipment. This research innovatively applies acoustic imaging technology to the field of partial discharge detection, providing a new technical approach for the condition assessment of power equipment, and possesses important theoretical value and engineering application prospects..
Key Words：Partial Discharge Detection；Acoustic Imaging Technology；Beamforming Algorithm；Deep Learning；Spatial Localization
目 录
摘 要 I
Abstract II
第1章 绪论 1
1.1 声学成像技术的研究背景 1
1.2 局部放电检测的意义与价值 1
1.3 国内外研究现状分析 1
1.4 本文研究方法与内容安排 2
第2章 声学成像原理与关键技术 3
2.1 声学成像的基本原理 3
2.2 关键信号处理技术分析 3
2.3 声源定位算法的研究进展 4
2.4 成像精度的影响因素探讨 4
第3章 局部放电声学特征提取与识别 6
3.1 局部放电的声学特性分析 6
3.2 特征参数提取方法研究 6
3.3 数据降噪与增强技术应用 7
3.4 放电模式分类与识别策略 7
第4章 基于声学成像的检测系统设计与实现 9
4.1 检测系统的整体架构设计 9
4.2 硬件设备选型与优化方案 9
4.3 软件平台开发与功能实现 10
4.4 实验验证与结果分析 10
结 论 12
参考文献 13
致 谢 14
