摘要
随着电力系统规模不断扩大和复杂性增加，传统故障预测方法难以满足现代电网安全稳定运行的需求。本研究旨在构建基于人工智能技术的电力系统故障预测与预警系统，以提高电力系统的可靠性与安全性。通过分析现有电力系统故障数据特征，提出一种融合深度学习与专家系统的混合智能算法，该算法能够有效处理非线性、时变性强的电力数据。利用卷积神经网络提取故障特征，结合长短期记忆网络实现对时间序列数据的精准建模，同时引入注意力机制增强模型对关键信息的捕捉能力。实验结果表明，所提方法在故障识别准确率上较传统方法提升15%以上，预警提前量平均达到30分钟，显著提高了故障预测的时效性和准确性。此外，系统设计了可视化界面，便于运维人员实时监控电网状态并及时采取预防措施。该研究不仅为电力系统故障预测提供了新的思路和技术手段，也为智能电网的发展奠定了理论基础，具有重要的实际应用价值和广阔的应用前景。
关键词：电力系统故障预测；混合智能算法；深度学习
Abstract
As the scale and complexity of power systems continue to expand, traditional fault prediction methods are increasingly unable to meet the demands for secure and stable operation of modern power grids. This study aims to develop an artificial intelligence-based fault prediction and early warning system for power systems to enhance their reliability and safety. By analyzing the characteristics of existing power system fault data, a hybrid intelligent algorithm integrating deep learning and expert systems is proposed, which can effectively handle nonlinear and highly time-varying power data. The proposed method employs convolutional neural networks for feature extraction of faults and combines long short-term memory networks to achieve precise modeling of time series data. Additionally, an attention mechanism is introduced to strengthen the model's ability to capture critical information. Experimental results demonstrate that the proposed method improves fault recognition accuracy by more than 15% compared to traditional methods, with an average lead time for warnings reaching 30 minutes, significantly enhancing the timeliness and accuracy of fault prediction. Furthermore, the system incorporates a visualization interface to facilitate real-time monitoring of grid status by maintenance personnel and enable timely preventive actions. This research not only provides new approaches and technical means for power system fault prediction but also lays a theoretical foundation for the development of smart grids, offering significant practical value and broad application prospects.
Keywords：Power System Fault Prediction; Hybrid Intelligent Algorithm; Deep Learning
目 录
摘要 I
Abstract II
一、绪论 1
(一) 电力系统故障预测的意义 1
(二) 国内外研究现状综述 1
(三) 本文研究方法与创新点 2
二、人工智能技术在电力系统的应用 2
(一) 人工智能算法概述 2
(二) 故障数据的获取与处理 3
(三) 人工智能模型的选择与构建 4
三、故障预测模型的设计与实现 4
(一) 预测模型架构设计 4
(二) 关键参数优化方法 5
(三) 模型训练与验证过程 6
四、预警系统的开发与集成 6
(一) 预警规则的制定 6
(二) 系统架构与模块划分 7
(三) 实时监控与预警发布 8
结 论 10
参考文献 11
随着电力系统规模不断扩大和复杂性增加，传统故障预测方法难以满足现代电网安全稳定运行的需求。本研究旨在构建基于人工智能技术的电力系统故障预测与预警系统，以提高电力系统的可靠性与安全性。通过分析现有电力系统故障数据特征，提出一种融合深度学习与专家系统的混合智能算法，该算法能够有效处理非线性、时变性强的电力数据。利用卷积神经网络提取故障特征，结合长短期记忆网络实现对时间序列数据的精准建模，同时引入注意力机制增强模型对关键信息的捕捉能力。实验结果表明，所提方法在故障识别准确率上较传统方法提升15%以上，预警提前量平均达到30分钟，显著提高了故障预测的时效性和准确性。此外，系统设计了可视化界面，便于运维人员实时监控电网状态并及时采取预防措施。该研究不仅为电力系统故障预测提供了新的思路和技术手段，也为智能电网的发展奠定了理论基础，具有重要的实际应用价值和广阔的应用前景。
关键词：电力系统故障预测；混合智能算法；深度学习
Abstract
As the scale and complexity of power systems continue to expand, traditional fault prediction methods are increasingly unable to meet the demands for secure and stable operation of modern power grids. This study aims to develop an artificial intelligence-based fault prediction and early warning system for power systems to enhance their reliability and safety. By analyzing the characteristics of existing power system fault data, a hybrid intelligent algorithm integrating deep learning and expert systems is proposed, which can effectively handle nonlinear and highly time-varying power data. The proposed method employs convolutional neural networks for feature extraction of faults and combines long short-term memory networks to achieve precise modeling of time series data. Additionally, an attention mechanism is introduced to strengthen the model's ability to capture critical information. Experimental results demonstrate that the proposed method improves fault recognition accuracy by more than 15% compared to traditional methods, with an average lead time for warnings reaching 30 minutes, significantly enhancing the timeliness and accuracy of fault prediction. Furthermore, the system incorporates a visualization interface to facilitate real-time monitoring of grid status by maintenance personnel and enable timely preventive actions. This research not only provides new approaches and technical means for power system fault prediction but also lays a theoretical foundation for the development of smart grids, offering significant practical value and broad application prospects.
Keywords：Power System Fault Prediction; Hybrid Intelligent Algorithm; Deep Learning
目 录
摘要 I
Abstract II
一、绪论 1
(一) 电力系统故障预测的意义 1
(二) 国内外研究现状综述 1
(三) 本文研究方法与创新点 2
二、人工智能技术在电力系统的应用 2
(一) 人工智能算法概述 2
(二) 故障数据的获取与处理 3
(三) 人工智能模型的选择与构建 4
三、故障预测模型的设计与实现 4
(一) 预测模型架构设计 4
(二) 关键参数优化方法 5
(三) 模型训练与验证过程 6
四、预警系统的开发与集成 6
(一) 预警规则的制定 6
(二) 系统架构与模块划分 7
(三) 实时监控与预警发布 8
结 论 10
参考文献 11
