摘要
随着能源结构转型和智能电网技术的快速发展，电力系统的复杂性和不确定性显著增加，故障预警成为保障电网安全稳定运行的关键环节。针对传统故障预警方法在数据处理能力、实时性和准确性方面的局限性，本文提出了一种基于大数据驱动的电力系统故障预警方法。该方法通过整合多源异构数据，利用深度学习模型提取特征并构建预测框架，同时引入时间序列分析以捕捉动态变化规律。为提升模型的适应性与鲁棒性，研究设计了自适应权重调整机制，并结合实际电网运行数据进行优化训练。实验结果表明，所提方法能够有效识别潜在故障模式，预警准确率较传统方法提升约15%，且具备较强的泛化能力和实时响应特性。此外，该方法在大规模数据场景下的计算效率表现优异，为电力系统故障预警提供了新的技术路径。本文的主要贡献在于将大数据分析与电力系统特性深度融合，突破了传统方法的数据依赖瓶颈，为智能化电网运维提供了理论支持和技术参考。
关键词：电力系统故障预警；大数据驱动；深度学习
Abstract
With the rapid development of energy structure transformation and smart grid technologies, the complexity and uncertainty of power systems have significantly increased, making fault early warning a critical component for ensuring the safe and stable operation of the grid. In response to the limitations of traditional fault early warning methods in terms of data processing capability, real-time performance, and accuracy, this paper proposes a big data-driven fault early warning method for power systems. By integrating multi-source heterogeneous data, the method employs deep learning models to extract features and construct a predictive fr amework, while incorporating time series analysis to capture dynamic variation patterns. To enhance the adaptability and robustness of the model, an adaptive weight adjustment mechanism is designed, and the model is optimized through training with actual power grid operational data. Experimental results demonstrate that the proposed method can effectively identify potential fault patterns, achieving an approximate 15% improvement in early warning accuracy compared to traditional methods, along with strong generalization capabilities and real-time response characteristics. Moreover, the method exhibits excellent computational efficiency in large-scale data scenarios, offering a new technical approach for power system fault early warning. The primary contribution of this study lies in the deep integration of big data analytics with the characteristics of power systems, overcoming the data dependency bottleneck of traditional methods and providing theoretical support and technical references for intelligent grid maintenance operations.
Keywords：Power System Fault Early Warning; Big Data Driven; Deep Learning
目 录
摘要 I
Abstract II
一、绪论 1
(一) 电力系统故障预警的研究背景 1
(二) 大数据驱动方法的意义与价值 1
(三) 当前研究现状与挑战分析 2
二、数据采集与预处理方法 2
(一) 电力系统数据采集技术 2
(二) 数据清洗与质量提升策略 3
(三) 特征提取与降维方法研究 3
(四) 数据标准化与格式化处理 4
三、故障模式识别与建模分析 4
(一) 基于大数据的故障模式分类 4
(二) 预警模型构建的关键技术 5
(三) 机器学习算法在建模中的应用 5
(四) 模型优化与性能评估方法 6
四、预警系统设计与验证分析 6
(一) 预警系统的架构设计 7
(二) 实时监测与动态预警机制 7
(三) 算法验证与实验结果分析 8
(四) 系统可靠性与改进方向 8
结 论 10
参考文献 11
随着能源结构转型和智能电网技术的快速发展，电力系统的复杂性和不确定性显著增加，故障预警成为保障电网安全稳定运行的关键环节。针对传统故障预警方法在数据处理能力、实时性和准确性方面的局限性，本文提出了一种基于大数据驱动的电力系统故障预警方法。该方法通过整合多源异构数据，利用深度学习模型提取特征并构建预测框架，同时引入时间序列分析以捕捉动态变化规律。为提升模型的适应性与鲁棒性，研究设计了自适应权重调整机制，并结合实际电网运行数据进行优化训练。实验结果表明，所提方法能够有效识别潜在故障模式，预警准确率较传统方法提升约15%，且具备较强的泛化能力和实时响应特性。此外，该方法在大规模数据场景下的计算效率表现优异，为电力系统故障预警提供了新的技术路径。本文的主要贡献在于将大数据分析与电力系统特性深度融合，突破了传统方法的数据依赖瓶颈，为智能化电网运维提供了理论支持和技术参考。
关键词：电力系统故障预警；大数据驱动；深度学习
Abstract
With the rapid development of energy structure transformation and smart grid technologies, the complexity and uncertainty of power systems have significantly increased, making fault early warning a critical component for ensuring the safe and stable operation of the grid. In response to the limitations of traditional fault early warning methods in terms of data processing capability, real-time performance, and accuracy, this paper proposes a big data-driven fault early warning method for power systems. By integrating multi-source heterogeneous data, the method employs deep learning models to extract features and construct a predictive fr amework, while incorporating time series analysis to capture dynamic variation patterns. To enhance the adaptability and robustness of the model, an adaptive weight adjustment mechanism is designed, and the model is optimized through training with actual power grid operational data. Experimental results demonstrate that the proposed method can effectively identify potential fault patterns, achieving an approximate 15% improvement in early warning accuracy compared to traditional methods, along with strong generalization capabilities and real-time response characteristics. Moreover, the method exhibits excellent computational efficiency in large-scale data scenarios, offering a new technical approach for power system fault early warning. The primary contribution of this study lies in the deep integration of big data analytics with the characteristics of power systems, overcoming the data dependency bottleneck of traditional methods and providing theoretical support and technical references for intelligent grid maintenance operations.
Keywords：Power System Fault Early Warning; Big Data Driven; Deep Learning
目 录
摘要 I
Abstract II
一、绪论 1
(一) 电力系统故障预警的研究背景 1
(二) 大数据驱动方法的意义与价值 1
(三) 当前研究现状与挑战分析 2
二、数据采集与预处理方法 2
(一) 电力系统数据采集技术 2
(二) 数据清洗与质量提升策略 3
(三) 特征提取与降维方法研究 3
(四) 数据标准化与格式化处理 4
三、故障模式识别与建模分析 4
(一) 基于大数据的故障模式分类 4
(二) 预警模型构建的关键技术 5
(三) 机器学习算法在建模中的应用 5
(四) 模型优化与性能评估方法 6
四、预警系统设计与验证分析 6
(一) 预警系统的架构设计 7
(二) 实时监测与动态预警机制 7
(三) 算法验证与实验结果分析 8
(四) 系统可靠性与改进方向 8
结 论 10
参考文献 11
