在直流输电系统中,换流阀阀基电子VBE(valve base electronics)设备的稳定运作对维护直流系统安全至关重要.传统的阀基电子设备电路板(VBE板)元件失效检测方法依赖于耗时的人工检查或基于规则的自动化系统,这些方法通常检测效率低下且准确性有限.针对该问题,提出一种基于改进的SqueezeNet深度学习模型的VBE板元件失效区域识别方法.通过引入深度可分离卷积和残差连接,所提改进SqueezeNet模型旨在提高元件失效检测的准确性,同时降低计算资源的需求.在VBE板元件失效数据集上的实验结果表明,所提方法在元件失效检测准确率和运算效率方面均优于传统方法和标准SqueezeNet模型,准确率达到了95.27％,比原模型高出4.45％.不仅提升了VBE板元件失效检测的效率和准确性,而且为电力系统中类似设备的元件失效诊断提供了新的技术参考.
Abstract
In a direct current(DC) power transmission system, the stable operation of a valve base electronics(VBE) device is crucial for its safety. However, the traditional methods for detecting the component failures in VBE device cir-cuit boards rely on time-consuming manual inspections or rule-based automation systems, which are often inefficient and limited in the detection accuracy. To address this problem, a method for identifying the component failure areas in VBE boards is proposed in this paper, which uses an enhanced SqueezeNet deep learning model. By incorporating depth-wise separable convolutions and residual connections, the enhanced SqueezeNet model aims to improve the accuracy of com-ponent failure detection while reducing the demand for computational resources. Experiments on a VBE board compo-nent failure dataset demonstrate that the proposed method outperforms the traditional methods and the standard SqueezeNet model in terms of detection accuracy and computational efficiency, and it achieves an accuracy rate of 95.27％, which is 4.45％ higher than that of the standard mod-el. The results of this research not only enhance the efficiency and accuracy of component failure detection in VBE boards, but also provide a novel technical reference for the diagnosis of component failures in similar equipment in power systems.
关键词
阀基电子设备/SqueezeNet模型/元件失效检测/特征提取
Key words
Valve base electronics(VBE) device/SqueezeNet model/component failure detection/feature extraction
维普
万方数据