直流输电系统中基于深度学习的电力质量监测与故障诊断方法

Power Quality Monitoring and Fault Diagnosis Method Based on Deep Learning in Direct Current Transmission System

通过深度学习算法对电力质量数据进行分析和诊断,以准确监测并识别直流输电系统中的故障和异常情况。采用卷积神经网络(Convolutional Neural Networks,CNN)深度学习模型,使用大量的电力质量数据进行训练和测试。通过实验和分析,深度学习模型具有很强的特征提取和分类能力,能够从复杂的电力质量数据中学习到有效的表示和模式。该方法在故障诊断的准确性和效率方面优于传统的方法,能够在实际的电力系统中进行故障预警和故障诊断,提高电力系统的可靠性和稳定性。

Through the deep learning algorithm,the power quality data are analyzed and diagnosed,so as to accurately monitor and identify faults and anomalies in the DC transmission system.Convolutional Neural Networks(CNN)deep learning model is adopted,and a large number of power quality data are used for training and testing.Through experiments and analysis,the deep learning model has strong ability of feature extraction and classification,and can learn effective representations and patterns from complex power quality data.This method is superior to the traditional methods in the accuracy and efficiency of fault diagnosis,and can be used for fault early warning and fault diagnosis in actual power systems,thus improving the reliability and stability of power systems.