A CNN recognition method for early stage of 10 kV single core cable based on sheath current

Chi, Peng; Zhang, Zhe; Liang, Rui; Cheng, Cheng; Chen, Shaokang

doi:10.1016/j.epsr.2020.106292

Cited by 14 publications

(12 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When the current runs in a high state for a long time, the corresponding data can be substituted into the equation (11), and the tolerance limit of cables and power equipment to the high current can be combined to determine whether the fault state is close.…”

Section: High Current Statementioning

confidence: 99%

“…Up to now, the research on current status analysis and fault diagnosis of cable and power equipment mainly focuses on fault monitoring, fault diagnosis, and fault identification. Moreover, it has become a trend to explore convenient, efficient, and reliable currentcarrying status monitoring and fault diagnosis methods [11,12]. In 2020, Chi et al proposed a deep convolutional neural network recognition algorithm based on the early stage of a 10 kV single-core cable based on sheath current.…”

Section: Introductionmentioning

confidence: 99%

“…In 2020, Chi et al proposed a deep convolutional neural network recognition algorithm based on the early stage of a 10 kV single-core cable based on sheath current. They simulated four cable states [11]. In 2021, Kai Chen et al proposed a cable temperature monitoring method based on distributed temperature sensors, which can effectively monitor cable heating faults and locate fault points [12].…”

Section: Introductionmentioning

confidence: 99%

“…So far, researchers have proposed many methods for current-carrying state analysis and fault diagnosis of power cables and equipment. However, most of them are based on one or more characteristics of the current or voltage signal, then using artificial intelligence, deep learning, and neural networks to identify the cable status [11], which have the disadvantages of large training sample size and noise. In addition, from the perspective of previous studies, there are few reports on realizing potential anomalies identifying and early fault warning during long-term operation.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Cable current-carrying status analysis and early fault warning method based on temperature information

Yang

Zhang

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

Long-term operation of cables and equipment under abnormal currents can cause insulation aging and reduce the service life of the equipment. Identifying the current-carrying status accurately and early detection of potential failures of power cables is particularly significant for ensuring the operation of power equipment safely and economically. This paper proposed a method for detecting the current-carrying status of power cables and an early fault warning based on temperature information. Based on the theoretical basis of the relationship between temperature and current. According to IEC 61000-3-7 and IEC 60038 standards, the current-carrying state evaluation method is analyzed and given, and the early fault warning model is established. Finally, the paper takes the cable connector and the lightning arrester in the power ring network cabinet as an example to verify the method's feasibility. It shows that the characteristic information of the relative temperature variation curves under different current carrying states significantly differs. Includes the slope of hour 9, the slope of hour 2, and the maximum. Then the total duration under abnormal current operation is calculated, and the early warning of faults can realize.

show abstract

Section: High Current Statementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Cable current-carrying status analysis and early fault warning method based on temperature information

Yang

Zhang

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…The authors of [ 12 , 13 , 14 ] used the cable broadband impedance spectrum processed by the BIS method to identify cables’ thermal aging defects, but the BIS method was only applied to cable defect diagnosis and did not identify cable faults such as open circuits and short circuits. The authors of [ 15 , 16 , 17 ] extracted fault features such as power waveforms, zero−sequence currents, and cable sheath currents’ abrupt change information, respectively, and used deep learning algorithms to construct classification models to identify fault types; the fault features extracted by the above methods required further calculations, and the fault diagnosis efficiency was low, the feature extraction process was more complicated, and no research was carried out on fault localization.…”

Section: Introductionmentioning

confidence: 99%

Fault Identification and Localization of a Time−Frequency Domain Joint Impedance Spectrum of Cables Based on Deep Belief Networks

Wan

Yuan

et al. 2023

Sensors

View full text Add to dashboard Cite

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.

show abstract

A Review on Incipient Fault Detection, Location and Classification in Underground Cable

Das,

Mishra,

Swain

et al. 2024

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

A CNN recognition method for early stage of 10 kV single core cable based on sheath current

Cited by 14 publications

References 20 publications

Cable current-carrying status analysis and early fault warning method based on temperature information

Cable current-carrying status analysis and early fault warning method based on temperature information

Fault Identification and Localization of a Time−Frequency Domain Joint Impedance Spectrum of Cables Based on Deep Belief Networks

A Review on Incipient Fault Detection, Location and Classification in Underground Cable

Contact Info

Product

Resources

About