| 引用本文: | 王小龙,蔡昊天,张浩博,等.融合参考值与改进生成对抗网络的台区负荷数据修复方法[J].电力系统保护与控制,2026,54(06):134-147. |
| WANG Xiaolong,CAI Haotian,ZHANG Haobo,et al.Load data restoration method for distribution transformer areas based on reference values and an improved generative adversarial network[J].Power System Protection and Control,2026,54(06):134-147 |
|
| 摘要: |
| 传统数据修复方法因忽略外部时空关联信息导致台区负荷修复精度受限。提出一种融合参考值与改进生成对抗网络的台区负荷数据修复方法。首先，提出一种参考值构建方法，将外部信息与负荷自身时空关联融合，生成逼近真实数据分布的修复起点。其次，在生成对抗网络结构中设计动态位置编码替代随机噪声，获取缺失数据上下文信息。接着嵌入混合残差空洞卷积及通道注意力机制，以增强模型负荷波动特征提取能力。再将多尺度结构相似性损失引入原始损失函数以优化训练效果。最后，基于实测数据验证，所提方法相较于传统数据修复方法修复精度提高约40%。 |
| 关键词: 低压台区 数据修复 参考值 生成对抗网络 动态位置编码 |
| DOI：10.19783/j.cnki.pspc.250805 |
| 分类号: |
| 基金项目:国家自然科学基金项目资助(62273104)；广东电网有限责任公司科技项目资助(GDKJXM20231545) |
|
| Load data restoration method for distribution transformer areas based on reference values and an improved generative adversarial network |
|
WANG Xiaolong, CAI Haotian, ZHANG Haobo, PENG Qingjiong, PENG Xiangang, ZHAO Zhuoli
|
|
School of Automation, Guangdong University of Technology, Guangzhou 510006, China
|
| Abstract: |
| Traditional data restoration methods are limited in accuracy for distribution transformer area load data because they neglect external spatiotemporal correlations. This paper proposes a load data restoration method that combines reference values and an improved generative adversarial network (GAN). First, a reference value construction method is developed, which integrates external information with the load’s own spatiotemporal correlations to generate an initial restoration point that approximates the true data distribution. Then, within the GAN structure, dynamic positional encoding is designed to replace random noise, enabling the model to capture contextual information of missing data. Hybrid residual dilated convolutions and a channel attention mechanisms are embedded to enhance the model’s capability in extracting load fluctuation features. In addition, a multi-scale structural similarity loss is introduced into the original loss function to optimize the training performance. Finally, validation based on real measured data shows that the proposed method improves restoration accuracy by approximately 40% compared to traditional data restoration methods. |
| Key words: low-voltage distribution transformer area data restoration referenced value generative adversarial network dynamic positional encoding |
