摘 要
随着智能电网的快速发展，电力系统产生了海量且复杂的数据，如何高效处理与深度分析这些大数据成为亟待解决的关键问题。本研究旨在探索智能电网中大数据处理与高级分析技术，以提升电力系统的运行效率、可靠性和智能化水平。通过构建基于云计算和分布式存储的大数据处理平台，采用数据挖掘、机器学习等方法对海量电力数据进行预处理、特征提取与模式识别。创新性地提出了一种融合多源异构数据的综合分析模型，实现了对电网运行状态的实时监测、故障预警及优化调度。实验结果表明，该技术方案能够显著提高数据处理速度，增强预测精度，降低误报率。研究结果为智能电网的建设提供了理论依据和技术支持，对于推动电力行业的数字化转型具有重要意义，主要贡献在于建立了适应智能电网需求的大数据分析框架，为后续研究奠定了基础。
关键词：智能电网；大数据处理；云计算
Abstract
With the rapid development of smart grids, power systems are generating massive and complex datasets, making efficient processing and in-depth analysis of this big data an urgent challenge. This study aims to explore big data processing and advanced analytical techniques in smart grids to enhance the operational efficiency, reliability, and intelligence level of power systems. By constructing a big data processing platform based on cloud computing and distributed storage, this research employs methods such as data mining and machine learning for preprocessing, feature extraction, and pattern recognition of large-scale power data. Innovatively, a comprehensive analysis model that integrates multi-source heterogeneous data is proposed, achieving real-time monitoring of grid operation status, fault warning, and optimized scheduling. Experimental results demonstrate that this technical solution can significantly improve data processing speed, enhance prediction accuracy, and reduce false alarm rates. The findings provide theoretical foundations and technical support for the construction of smart grids, playing a crucial role in promoting the digital transformation of the power industry. The primary contribution lies in establishing a big data analytics fr amework tailored to the needs of smart grids, laying the groundwork for future research.
Keywords: Smart Grid；Big Data Processing；Cloud Computing
目 录
引言 1
一、智能电网大数据概述 1
（一）智能电网数据来源 1
（二）数据特征分析 2
（三）大数据处理挑战 2
二、数据采集与预处理技术 3
（一）采集系统架构 3
（二）数据清洗方法 3
（三）预处理算法优化 4
三、数据存储与管理策略 4
（一）分布式存储方案 4
（二）数据索引机制 4
（三）存储性能评估 5
四、高级数据分析与应用 5
（一）负荷预测模型 5
（二）故障诊断技术 6
（三）决策支持系统 6
结 论 7
致 谢 8
参考文献 9
随着智能电网的快速发展，电力系统产生了海量且复杂的数据，如何高效处理与深度分析这些大数据成为亟待解决的关键问题。本研究旨在探索智能电网中大数据处理与高级分析技术，以提升电力系统的运行效率、可靠性和智能化水平。通过构建基于云计算和分布式存储的大数据处理平台，采用数据挖掘、机器学习等方法对海量电力数据进行预处理、特征提取与模式识别。创新性地提出了一种融合多源异构数据的综合分析模型，实现了对电网运行状态的实时监测、故障预警及优化调度。实验结果表明，该技术方案能够显著提高数据处理速度，增强预测精度，降低误报率。研究结果为智能电网的建设提供了理论依据和技术支持，对于推动电力行业的数字化转型具有重要意义，主要贡献在于建立了适应智能电网需求的大数据分析框架，为后续研究奠定了基础。
关键词：智能电网；大数据处理；云计算
Abstract
With the rapid development of smart grids, power systems are generating massive and complex datasets, making efficient processing and in-depth analysis of this big data an urgent challenge. This study aims to explore big data processing and advanced analytical techniques in smart grids to enhance the operational efficiency, reliability, and intelligence level of power systems. By constructing a big data processing platform based on cloud computing and distributed storage, this research employs methods such as data mining and machine learning for preprocessing, feature extraction, and pattern recognition of large-scale power data. Innovatively, a comprehensive analysis model that integrates multi-source heterogeneous data is proposed, achieving real-time monitoring of grid operation status, fault warning, and optimized scheduling. Experimental results demonstrate that this technical solution can significantly improve data processing speed, enhance prediction accuracy, and reduce false alarm rates. The findings provide theoretical foundations and technical support for the construction of smart grids, playing a crucial role in promoting the digital transformation of the power industry. The primary contribution lies in establishing a big data analytics fr amework tailored to the needs of smart grids, laying the groundwork for future research.
Keywords: Smart Grid；Big Data Processing；Cloud Computing
目 录
引言 1
一、智能电网大数据概述 1
（一）智能电网数据来源 1
（二）数据特征分析 2
（三）大数据处理挑战 2
二、数据采集与预处理技术 3
（一）采集系统架构 3
（二）数据清洗方法 3
（三）预处理算法优化 4
三、数据存储与管理策略 4
（一）分布式存储方案 4
（二）数据索引机制 4
（三）存储性能评估 5
四、高级数据分析与应用 5
（一）负荷预测模型 5
（二）故障诊断技术 6
（三）决策支持系统 6
结 论 7
致 谢 8
参考文献 9
