School of Electric and Control Engineering, North China University of Technology, Beijing100144, China.
Key Account Division, Beijing Aerospace Data Stock Company National Big-Data Application Technology, Beijing100044, China.
Sensors (Basel). 2023 Jan 6;23(2):684. doi: 10.3390/s23020684.
To improve the accuracy of shallow neural networks in processing complex signals and cable fault diagnosis, and to overcome the shortage of manual dependency and cable fault feature extraction, a deep learning method is introduced, and a time-frequency domain joint impedance spectrum is proposed for cable fault identification and localization based on a deep belief network (DBN). Firstly, based on the distribution parameter model of power cables, we model and analyze the cables under normal operation and different fault types, and we obtain the headend input impedance spectrum and the headend input time-frequency domain impedance spectrum of cables under various operating conditions. The headend input impedance amplitude and phase of normal operation and different fault cables are extracted as the original input samples of the cable fault type identification model; the real part of the headend input time-frequency domain impedance of the fault cables is extracted as the original input samples of the cable fault location model. Then, the unsupervised pre-training and supervised inverse fine-tuning methods are used for automatically learning, training, and extracting the cable fault state features from the original input samples, and the DBN-based cable fault type recognition model and location model are constructed and used to realize the type recognition and location of cable faults. Finally, the proposed method is validated by simulation, and the results show that the method has good fault feature extraction capability and high fault type recognition and localization accuracy.
为提高浅层神经网络处理复杂信号和电缆故障诊断的准确性，克服人工依赖性和电缆故障特征提取的不足，引入深度学习方法，提出了一种基于深度置信网络（DBN）的电缆故障识别和定位的时频域联合阻抗谱。首先，基于电力电缆的分布参数模型，对正常运行和不同故障类型下的电缆进行建模和分析，得到各种运行条件下的电缆头端输入阻抗谱和电缆头端输入时频域阻抗谱。提取正常运行和不同故障电缆的头端输入阻抗幅相作为电缆故障类型识别模型的原始输入样本；提取故障电缆头端输入时频域阻抗的实部作为电缆故障定位模型的原始输入样本。然后，采用无监督预训练和有监督逆微调方法，从原始输入样本中自动学习、训练和提取电缆故障状态特征，构建基于 DBN 的电缆故障类型识别模型和定位模型，实现电缆故障的类型识别和定位。最后，通过仿真验证了所提方法，结果表明该方法具有良好的故障特征提取能力和较高的故障类型识别和定位精度。
