电气设备寿命预测方法的研究 -电气工程及其自动化专业

摘要

随着电力系统规模的不断扩大和复杂程度的日益提高，电气设备的安全稳定运行对整个系统的可靠性和经济性至关重要，而准确预测电气设备寿命是保障其安全稳定运行的关键环节。为此，本文旨在研究电气设备寿命预测方法，以期为电力系统的运维提供理论依据和技术支持。基于多源数据融合技术，综合考虑电气设备的运行工况、环境因素以及历史故障信息等多方面影响因素，提出一种融合深度学习与传统统计模型的混合预测方法。该方法利用深度学习强大的特征提取能力挖掘数据深层规律，同时结合传统统计模型的优势确保预测结果的可解释性。通过对比实验验证，相较于单一模型，所提方法在预测精度上提高了约15%，且能有效识别出影响电气设备寿命的关键因素。这一成果不仅为电气设备寿命预测提供了新的思路，还为电力系统中其他类似问题的研究奠定了基础，有助于推动电力系统智能化运维的发展进程。

关键词：电气设备寿命预测多源数据融合深度学习

Abstract

As the scale and complexity of power systems continue to expand, the safe and stable operation of electrical equipment becomes critical for the reliability and economic efficiency of the entire system. Accurate prediction of the lifespan of electrical equipment is a key component in ensuring its safe and stable operation. This study aims to investigate methods for predicting the lifespan of electrical equipment, providing theoretical support and technical guidance for the operation and maintenance of power systems. By integrating multi-source data fusion technology, this research proposes a hybrid prediction method that combines deep learning with traditional statistical models, considering multiple influencing factors such as operational conditions, environmental factors, and historical fault information. The proposed method leverages the strong feature extraction capabilities of deep learning to uncover deeper patterns in the data while incorporating the advantages of traditional statistical models to ensure the interpretability of the prediction results. Comparative experiments demonstrate that the proposed method achieves approximately 15% higher prediction accuracy compared to single models and effectively identifies key factors influencing the lifespan of electrical equipment. This outcome not only offers new insights into the prediction of electrical equipment lifespan but also lays a foundation for researching similar issues within power systems, contributing to the advancement of intelligent operation and maintenance in power systems.

Keyword:Electrical Equipment Life Prediction Multi-Source Data Fusion Deep Learning

1绪论 1

1.1研究背景与意义 1

1.2国内外研究现状 1

1.3本文研究方法 2

2寿命预测理论基础 2

2.1电气设备老化机理 2

2.2常用寿命预测模型 3

2.3数据采集与处理方法 3

3关键影响因素分析 4

3.1环境因素对寿命的影响 4

3.2运行状态对寿命的影响 4