基于深度学习的配电网线损异常检测方法
A Deep Iearning Based Method for Detecting Abnormal Line Loss in Distribution Networks
刘晶1
作者信息
- 1. 国网清苑县供电公司,河北保定 071100
- 折叠
摘要
配电网的线损异常检测对于保障电网安全、优化资源分配和提升服务质量至关重要.传统的线损分析方法在处理大规模数据集和识别复杂异常模式时存在局限性,针对这一问题,文章提出了一种基于深度学习的配电网线损异常检测方法.该方法利用LSTM对线损数据进行时序分析以捕捉数据的时空特征,实现对配电网线损的实时监测和异常检测.试验结果表明,该方法具有较高的检测精度和实时性,能有效识别出配电网中的线损异常,为配电网的优化运行和管理提供了有力的技术支持.
Abstract
The detection of abnormal line losses in distribution networks is crucial for ensuring grid safety,optimizing resource allocation,and improving service quality.Traditional line loss analysis methods have limitations in handling large-scale datasets and identifying complex abnormal patterns.To address this issue,this article proposes a deep learning based method for detecting abnormal line losses in distribution networks.This method uses LSTM to perform temporal analysis on line loss data to capture the spatiotemporal characteristics of the data,achieving real-time monitoring and anomaly detection of distribution line losses.The experimental results show that this method has high detection accuracy and real-time performance,and can effectively identify line loss anomalies in the distribution network,providing strong technical support for the optimized operation and management of the distribution network.
关键词
深度学习/配电网/线损异常检测/长短期记忆网络Key words
deep learning/distribution network/abnormal detection of line loss/long short-term memory network引用本文复制引用
出版年
2024
NETL
NSTL
万方数据