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基于生成对抗Transformer的电力负荷数据异常检测

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  • 万方数据
  • 维普
电力负荷异常数据将给电力系统规划、负荷预测以及用能分析等带来较大的负面影响,因此亟须对负荷数据异常进行检测与识别.首先,针对电力负荷数据异常分类、原因及其特征开展分析.其次,改进传统Transformer编码器结构,采用多头注意力层代替掩码多头注意力层,同时移除前馈网络,以提高模型对负荷时序序列的全局注意力.基于生成对抗网络(generative adversarial networks,GAN)生成器与判别器的博弈结构,提出一种改进的GAN-Transformer模型,以更好地捕捉趋势性特征并加速模型收敛.然后,引入多阶段映射与训练方法,综合焦点分数打分机制,通过分阶段负荷序列重构帮助模型更好地提取负荷数据异常特征.最后,算例分析结果表明,GAN-Trans-former模型在负荷数据异常检测精确率、召回率、F1值以及训练时间方面均具有更优的性能,验证了所提方法的有效性和优越性.文中研究工作为基于深度学习进一步实现电力负荷数据异常分类与数据修复提供了有益参考.
Anomaly detection of power load data based on Transformer and generative adversarial networks
Power system planning,load forecasting,and energy utilization analysis are significantly impacted by power load anomalies,necessitating prompt detection and identification.Firstly,the abnormal classification,causes,and characteristics of power load data are analyzed.Secondly,it enhances the traditional Transformer encoder structure by replacing the mask multi-header attention layer with the multi-header attention layer and eliminating the feedforward network.These improvements aim to enhance the model's global attention to the load sequence.To capture trend characteristics more effectively and expedite convergence,an improved generative adversarial networks(GAN)-Transformer model is proposed based on the generator and discriminator game structure of traditional GAN.Additionally,a multi-stage mapping and training approach along with an integrated focus score scoring mechanism are introduced.These techniques facilitate phased load sequence reconstruction,enabling the model to better extract anomalous features of the load data.Finally,through an analysis of an arithmetic example,the results demonstrate that the GAN-Transformer model outperforms in terms of load data anomaly detection accuracy,recall,F1 value,and training time.These results validate the effectiveness and superiority of the proposed method.The findings of this research provide valuable insights for advancements in power load data anomaly classification and data repair based on deep learning.

power load datadata anomaly detectiongenerative adversarial networks(GAN)-Transformermulti-stage training and mappingfocus scoresequence refactoring

陆旦宏、范文尧、杨婷、倪敏珏、李思琦、朱晓

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南京工程学院电力工程学院,江苏南京 211167

电力负荷数据 数据异常检测 生成对抗网络(GAN)-Transformer 多阶段训练与映射 焦点分数 序列重构

江苏省自然科学基金资助项目江苏省产学研合作项目

BK20210932BY2022056

2024

10.12158/j.2096-3203.2024.01.017

电力工程技术
江苏省电力公司 江苏省电机工程学会

电力工程技术

CSTPCD北大核心
影响因子：0.969
ISSN：2096-3203
年,卷(期)：2024.43(1)
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