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，这对数据处理与分析技术提出了新的挑战。为提高智能电网运行效率、可靠性及安全性，本研究聚焦于大数据处理与分析技术在智能电网中的应用。通过深入剖析智能电网数据特征，结合分布式计算框架Hadoop和Spark，提出一种融合多源异构数据的高效处理方法，该方法能够实现对大规模电力数据的快速存储、索引与查询。同时，引入深度学习算法构建负荷预测模型，有效提升了短期负荷预测精度，较传统方法平均绝对百分比误差降低约15%。此外，基于图数据库设计了电网拓扑结构分析系统，实现了对电网故障的实时监测与定位，故障识别准确率达到90%以上。本研究创新性地将大数据技术与智能电网业务深度融合，不仅为电力系统的优化调度提供了决策支持，还为智能电网相关研究开辟了新思路，在推动智能电网智能化转型方面具有重要意义。
关键词：智能电网；大数据处理；分布式计算
Abstract
With the rapid development of smart grids, power systems are generating massive and complex data, posing new challenges to data processing and analysis technologies. To enhance the operational efficiency, reliability, and security of smart grids, this study focuses on the application of big data processing and analysis techniques in smart grids. By thoroughly analyzing the characteristics of smart grid data and integrating distributed computing fr ameworks such as Hadoop and Spark, a highly efficient method for processing multi-source heterogeneous data is proposed, which enables rapid storage, indexing, and querying of large-scale power data. Meanwhile, deep learning algorithms are introduced to construct load forecasting models, effectively improving short-term load forecasting accuracy with an average absolute percentage error reduction of approximately 15% compared to traditional methods. Additionally, a graph database-based system for power grid topology analysis has been designed, achieving real-time monitoring and location of grid faults with a fault identification accuracy rate exceeding 90%. This research innovatively integrates big data technology with smart grid operations, not only providing decision support for the optimal scheduling of power systems but also opening up new avenues for related research, playing a significant role in promoting the intelligent transformation of smart grids.
Keywords：Smart Grid; Big Data Processing; Distributed Computing
目 录
摘要 I
Abstract II
一、绪论 1
(一) 智能电网大数据研究背景与意义 1
(二) 国内外研究现状综述 1
(三) 本文研究方法与技术路线 2
二、智能电网大数据处理技术 2
(一) 数据采集与预处理技术 2
(二) 数据存储与管理策略 3
(三) 数据清洗与质量控制方法 4
三、智能电网大数据分析方法 4
(一) 数据挖掘与模式识别 4
(二) 预测建模与优化算法 5
(三) 实时数据分析与决策支持 6
四、大数据技术在智能电网的应用 7
(一) 电力系统运行监控应用 7
(二) 故障诊断与预防性维护 7
(三) 用户侧需求响应分析 8
结 论 10
参考文献 11
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，这对数据处理与分析技术提出了新的挑战。为提高智能电网运行效率、可靠性及安全性，本研究聚焦于大数据处理与分析技术在智能电网中的应用。通过深入剖析智能电网数据特征，结合分布式计算框架Hadoop和Spark，提出一种融合多源异构数据的高效处理方法，该方法能够实现对大规模电力数据的快速存储、索引与查询。同时，引入深度学习算法构建负荷预测模型，有效提升了短期负荷预测精度，较传统方法平均绝对百分比误差降低约15%。此外，基于图数据库设计了电网拓扑结构分析系统，实现了对电网故障的实时监测与定位，故障识别准确率达到90%以上。本研究创新性地将大数据技术与智能电网业务深度融合，不仅为电力系统的优化调度提供了决策支持，还为智能电网相关研究开辟了新思路，在推动智能电网智能化转型方面具有重要意义。
关键词：智能电网；大数据处理；分布式计算
Abstract
With the rapid development of smart grids, power systems are generating massive and complex data, posing new challenges to data processing and analysis technologies. To enhance the operational efficiency, reliability, and security of smart grids, this study focuses on the application of big data processing and analysis techniques in smart grids. By thoroughly analyzing the characteristics of smart grid data and integrating distributed computing fr ameworks such as Hadoop and Spark, a highly efficient method for processing multi-source heterogeneous data is proposed, which enables rapid storage, indexing, and querying of large-scale power data. Meanwhile, deep learning algorithms are introduced to construct load forecasting models, effectively improving short-term load forecasting accuracy with an average absolute percentage error reduction of approximately 15% compared to traditional methods. Additionally, a graph database-based system for power grid topology analysis has been designed, achieving real-time monitoring and location of grid faults with a fault identification accuracy rate exceeding 90%. This research innovatively integrates big data technology with smart grid operations, not only providing decision support for the optimal scheduling of power systems but also opening up new avenues for related research, playing a significant role in promoting the intelligent transformation of smart grids.
Keywords：Smart Grid; Big Data Processing; Distributed Computing
目 录
摘要 I
Abstract II
一、绪论 1
(一) 智能电网大数据研究背景与意义 1
(二) 国内外研究现状综述 1
(三) 本文研究方法与技术路线 2
二、智能电网大数据处理技术 2
(一) 数据采集与预处理技术 2
(二) 数据存储与管理策略 3
(三) 数据清洗与质量控制方法 4
三、智能电网大数据分析方法 4
(一) 数据挖掘与模式识别 4
(二) 预测建模与优化算法 5
(三) 实时数据分析与决策支持 6
四、大数据技术在智能电网的应用 7
(一) 电力系统运行监控应用 7
(二) 故障诊断与预防性维护 7
(三) 用户侧需求响应分析 8
结 论 10
参考文献 11
