摘要
随着电力系统规模的不断扩大和复杂性的持续提升，电气设备的安全稳定运行对电网可靠性至关重要，而传统基于周期性检修的方式已难以满足现代电力系统的需求。为此，本研究聚焦于电气设备状态监测与健康管理技术，旨在通过先进的信号采集、数据分析及预测模型，实现对设备健康状态的实时评估与故障预警。研究采用多源数据融合方法，结合机器学习算法与领域知识，构建了一套适用于复杂工况的电气设备健康评估体系。通过对典型电气设备如变压器、断路器等的实际案例分析，验证了所提方法在故障早期识别与趋势预测中的有效性。结果表明，该技术能够显著提高状态监测的精度，并为优化检修策略提供科学依据。本研究的主要创新点在于提出了一种融合时域特征与频域特征的状态特征提取方法，以及基于深度学习的自适应健康指数计算模型，有效解决了传统方法在非线性、非平稳信号处理中的不足。研究成果为推动电气设备智能化运维提供了重要技术支持，具有广泛的应用前景。
关键词：电气设备健康评估；状态监测；多源数据融合
Abstract
With the continuous expansion and increasing complexity of power systems, the safe and stable operation of electrical equipment has become critical to grid reliability. Traditional periodic maintenance approaches are no longer sufficient to meet the demands of modern power systems. This study focuses on condition monitoring and health management technologies for electrical equipment, aiming to achieve real-time health assessment and fault prediction through advanced signal acquisition, data analysis, and predictive modeling. By employing multi-source data fusion methods combined with machine learning algorithms and domain knowledge, a comprehensive health evaluation system suitable for complex operating conditions has been developed. Case studies involving typical electrical equipment such as transformers and circuit breakers have validated the effectiveness of the proposed method in early fault identification and trend prediction. The results demonstrate that this technology significantly improves the accuracy of condition monitoring and provides a scientific basis for optimizing maintenance strategies. Key innovations of this research include the development of a state feature extraction method that integrates time-domain and frequency-domain characteristics, as well as an adaptive health index calculation model based on deep learning, effectively addressing the limitations of traditional methods in processing nonlinear and non-stationary signals. This research offers important technical support for advancing intelligent operation and maintenance of electrical equipment, presenting broad application prospects.
Keywords：Electrical Equipment Health Assessment; Condition Monitoring
目 录
摘要 I
Abstract II
一、绪论 1
(一) 电气设备状态监测的研究背景与意义 1
(二) 国内外研究现状分析 1
(三) 本文研究方法与技术路线 2
二、电气设备状态监测关键技术研究 2
(一) 状态监测的数据采集技术 2
(二) 数据处理与特征提取方法 3
(三) 异常检测与故障诊断算法 3
三、健康管理技术的理论与应用 4
(一) 设备健康评估指标体系构建 4
(二) 基于数据驱动的健康状态预测 4
(三) 健康管理策略优化方法 5
四、状态监测与健康管理的集成方案设计 6
(一) 集成系统的架构设计 6
(二) 关键技术实现与验证 6
(三) 实际案例分析与效果评价 7
结 论 9
参考文献 10
随着电力系统规模的不断扩大和复杂性的持续提升，电气设备的安全稳定运行对电网可靠性至关重要，而传统基于周期性检修的方式已难以满足现代电力系统的需求。为此，本研究聚焦于电气设备状态监测与健康管理技术，旨在通过先进的信号采集、数据分析及预测模型，实现对设备健康状态的实时评估与故障预警。研究采用多源数据融合方法，结合机器学习算法与领域知识，构建了一套适用于复杂工况的电气设备健康评估体系。通过对典型电气设备如变压器、断路器等的实际案例分析，验证了所提方法在故障早期识别与趋势预测中的有效性。结果表明，该技术能够显著提高状态监测的精度，并为优化检修策略提供科学依据。本研究的主要创新点在于提出了一种融合时域特征与频域特征的状态特征提取方法，以及基于深度学习的自适应健康指数计算模型，有效解决了传统方法在非线性、非平稳信号处理中的不足。研究成果为推动电气设备智能化运维提供了重要技术支持，具有广泛的应用前景。
关键词：电气设备健康评估；状态监测；多源数据融合
Abstract
With the continuous expansion and increasing complexity of power systems, the safe and stable operation of electrical equipment has become critical to grid reliability. Traditional periodic maintenance approaches are no longer sufficient to meet the demands of modern power systems. This study focuses on condition monitoring and health management technologies for electrical equipment, aiming to achieve real-time health assessment and fault prediction through advanced signal acquisition, data analysis, and predictive modeling. By employing multi-source data fusion methods combined with machine learning algorithms and domain knowledge, a comprehensive health evaluation system suitable for complex operating conditions has been developed. Case studies involving typical electrical equipment such as transformers and circuit breakers have validated the effectiveness of the proposed method in early fault identification and trend prediction. The results demonstrate that this technology significantly improves the accuracy of condition monitoring and provides a scientific basis for optimizing maintenance strategies. Key innovations of this research include the development of a state feature extraction method that integrates time-domain and frequency-domain characteristics, as well as an adaptive health index calculation model based on deep learning, effectively addressing the limitations of traditional methods in processing nonlinear and non-stationary signals. This research offers important technical support for advancing intelligent operation and maintenance of electrical equipment, presenting broad application prospects.
Keywords：Electrical Equipment Health Assessment; Condition Monitoring
目 录
摘要 I
Abstract II
一、绪论 1
(一) 电气设备状态监测的研究背景与意义 1
(二) 国内外研究现状分析 1
(三) 本文研究方法与技术路线 2
二、电气设备状态监测关键技术研究 2
(一) 状态监测的数据采集技术 2
(二) 数据处理与特征提取方法 3
(三) 异常检测与故障诊断算法 3
三、健康管理技术的理论与应用 4
(一) 设备健康评估指标体系构建 4
(二) 基于数据驱动的健康状态预测 4
(三) 健康管理策略优化方法 5
四、状态监测与健康管理的集成方案设计 6
(一) 集成系统的架构设计 6
(二) 关键技术实现与验证 6
(三) 实际案例分析与效果评价 7
结 论 9
参考文献 10
