通过机器学习在线验证和管理电力系统中网关计流量的管理方案
Chong Li1, Hao Wang1, Hongtao Shen1
1State Grid Hebei Marketing Service Center, Shijiazhuang, HeBei, China.
PeerJ. Computer science
|December 11, 2023
概括
本研究引入了用于在线电能计验证的机器学习,提高了效率和准确性. 它使用人工智能模型进行拨号读取识别和错误估计,从而实现实时电力系统管理.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 电力系统 电力系统
背景情况:
- 传统的电能计校准是手动的,耗时的,容易出现错误.
- 现有的方法缺乏电力系统门户计流量的效率和实时监控能力.
研究的目的:
- 利用机器学习开发一个高效和准确的在线验证系统,用于电能计.
- 通过智能数据分析,增强电力系统中网关计流量的管理.
主要方法:
- 使用快速区域卷积神经网络 (Faster-RCNN) 和单拍多盒探测器 (SSD) 进行拨号读取识别.
- 实施数据预处理以排除轻载条件并建立估计错误模型.
- 使用有限内存递归最小平方算法 (LMRLSA) 验证估计准确性.
主要成果:
- 在使用528米读图片的拨号读取识别中实现了98.49%的准确性.
- 确定了精确估计电表误差的最佳参数:内存长度 (600-1,200) 和线路损失误差 (<5%).
- 证明有效消除旧数据对错误估计的影响,增强实时监控.
结论:
- 拟议的机器学习方法显著提高了电能计验证的效率和准确性.
- 在线监测和管理电力系统网关计流量是可行的开发的系统.
- 排除轻载数据和优化内存长度对于精确的错误估计至关重要.
相关概念视频
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