基于大数据通信技术的电能计量仪表异常并行检测方法

Parallel Detection Method of Electric Energy Metering Instrument Anomaly Based on Big Data Communication Technology

随着大数据技术的发展,电力系统对电能计量仪表的精确度和可靠性要求越来越高。传统的电能计量仪表异常检测方法往往存在响应时间长、检测准确度不足的问题。为了解决这些问题,提出一种基于大数据通信技术的电能计量仪表异常并行检测方法。该方法通过结合物联网传感器、快速傅里叶变换、主成分分析及Apache Spark分布式计算框架等,实现对电能计量数据的实时采集、预处理、特征提取和并行检测,提高了异常检测的准确度和效率,为电力系统的稳定运行和优化管理提供了坚实的技术支撑。

With the development of big data technology,the power system has increasingly high requirements for the accuracy and reliability of electric energy metering instrument.Traditional methods for detecting anomalies in electric energy metering instrument often suffer from long response times and insufficient detection accuracy.To address these issues,a parallel anomaly detection method for electric energy metering instrument based on big data communication technology is proposed.This method combines Internet of Things sensors,fast Fourier transform,principal component analysis,and Apache Spark distributed computing framework to achieve real-time collection,preprocessing,feature extraction,and parallel detection of energy metering data,improving the accuracy and efficiency of anomaly detection and providing solid technical support for the stable operation and optimized management of power systems.