基于改进的变压器和对策编码器的不平衡功率异常检测模型
1Marketing Service Center, State Grid Shanxi Electric Power Co. Ltd, Taiyuan, 030000, China. sk638x170907@163.com.
Scientific reports
|December 16, 2025
概括
本研究介绍了一种混合变压器-对抗式自动编码器模型,用于智能电网异常检测. 它提高了少数类的识别,并减少了计算负载,以实现更安全的电网运行.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 智能电网异常检测与不平衡的数据,高计算复杂性和稀缺的异常样本作斗争.
- 现有的方法往往表现出较低的少数阶级认可和模型偏差.
研究的目的:
- 开发一种新的混合架构,用于增强智能电网异常检测.
- 为了应对数据不平衡,计算复杂性和异常检测中的模型偏差的挑战.
主要方法:
- 一种混合架构,将增强的变压器与对抗式自动编码器 (AAE) 结合起来.
- 整合局部敏感哈希 (LSH) 注意与焦点损失与温度 (FLT) 的特征集群.
- 实现一个动态权重模块 (空间时间特征解网络 - STFDN) 适应性梯度调整.
- 频谱规范化的应用,以减少节点序列的内存使用.
主要成果:
- 使用光谱规范化将内存使用量减少了52.4% (从18.7GB降至8.9GB).
- 在Wasserstein距离限制下,FID得分提高了10.4%,达到28.4.
- 在使用动态温度缩放的SGSC数据集上将AUPRC提升到0.837.
- 在UK-DALE数据集上获得了89.3%的F1得分,推断延迟为183ms,适合边缘部署.
结论:
- 拟议的混合模型有效地解决了智能电网异常检测的关键挑战.
- 新型架构在准确性,效率和计算资源利用方面取得了显著的改进.
- 这项研究为新一代自动化电网运行和维护工具铺平了道路.
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