Binary classification model based on machine learning algorithm for the DC serial arc detection in electric vehicle battery system

Xia, Kun; Guo, Haotian; He, Sheng; Wei, Yu; Xu, Jingjun; Dong, Hui

doi:10.1049/iet-pel.2018.5789

Cited by 20 publications

(7 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The voltage would suddenly rise and the current would suddenly drop when an arc fault occurs in the circuit [24]. Owing to the location of the arc is difficult to determine, it is impossible to detect the voltage change.…”

Section: Characteristic In the Time Domainmentioning

confidence: 99%

“…Finally, the accuracy and false detection rate of training models for different data sets under different loads are obtained by experiments. Comparing to paper [24], pure resistance loads are not considered in this paper causing there are barely such loads in the EV power system. Actually, component of resistance exists both in the circuit of motor and DC/DC converter.…”

Section: Experimental Analysismentioning

confidence: 99%

“…What's more, the results would be deviated by switching noise, harmonic and variation in loads. In [24], a binary classification model based on machine learning has been applied to distinguish the frequency domain of arc current and the normal. The results show that the algorithm has well accuracy and robustness under different loads but with a risk of false detection, which would affect the normal operation of the system.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Wavelet entropy analysis and machine learning classification model of DC serial arc fault in electric vehicle power system

Xia

Liu

et al. 2019

IET Power Electronics

Self Cite

View full text Add to dashboard Cite

Electric vehicle (EV) power system is flammable and explosive when the direct current (DC) arc occurs at elevated temperature. Thus, DC serial arc real-time monitoring is an insurance to keep away from disaster. In this study, the detection algorithm of DC serial arc is proposed. The wavelet entropy algorithm, the classification model based on support vector machine and logistic regression are analysed separately. The above algorithms are combined to identify the DC serial arc faults effectively under different types of loads in EV power system. The results show that the combined algorithm has a good performance of DC serial arc detection with high accuracy and robustness compared with a simple approach. Meanwhile, the false detection rate of the detection algorithm is close to zero, which could ensure the safety and stable operation of the system.

show abstract

Section: Characteristic In the Time Domainmentioning

confidence: 99%

Section: Experimental Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Wavelet entropy analysis and machine learning classification model of DC serial arc fault in electric vehicle power system

Xia

Liu

et al. 2019

IET Power Electronics

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, the current will decrease when the serial arc occurs. Then, conventional protective devices will not be triggered, and the network is in a great dangerous situation [3]. Therefore, detecting serial arc fault correctly and timely is a vital task in maintaining the stable operation of DC systems.…”

Section: Introductionmentioning

confidence: 99%

Different Domains Based Machine and Deep Learning Diagnosis for DC Series Arc Failure

Dang

Kwak²,

Choi

2021

IEEE Access

View full text Add to dashboard Cite

Series arc faults are becoming more dangerous in DC systems. Without detecting in time and separation correctly, these fault events can cause electrical fires or explosions, creating a massive threat to people's safety and properties. This paper presents an analysis and comparison of DC series arc fault detection using various artificial intelligence (AI) algorithms in DC systems. The combinations of six feature parameters in both time and frequency domains with various AI techniques are recommended to detect DC series arc fault effectively. The performance and effectiveness of different combinations between feature parameters and learning techniques are summarized and discussed. Finally, practical challenges are identified, and suitable combinations of feature parameters and learning techniques are recommended for different operation conditions.

show abstract

“…These papers mainly analyse the current harmonics when arc fault occurs. Short‐time Fourier transform or wavelet transform are used to analyse the current frequency band [11, 12]. In 2018, Qing Xiong et al used discrete wavelet transform to analyse the spectrum of capacitor current.…”

Section: Introductionmentioning

confidence: 99%

Data‐enhanced machine recognition model of DC serial arc in electric vehicle power system

Xia

Zhang

Liu

et al. 2020

IET power electron.

Self Cite

View full text Add to dashboard Cite

Electric vehicle (EV) power system is the key to the development of EVs. If direct current (DC) arc occurs in the power system, it is difficult to extinguish at zero point. The arc fault will release a huge amount of energy and continuous sparking, which may cause spontaneous combustion or even explosion. In this study, an arc detection algorithm based on the classification of windowed Fourier transform and support vector machine (SVM) model is proposed for DC serial arc detection of EV power system. In order to optimise the arc detection algorithm, the authors use the pre-detection algorithm, which can effectively reduce the false detection rate of DC arc fault and ensure the reliability of detection algorithm. In addition, they propose an arc fault data enhancement model, which can generate arc fault current data. Finally, the experimental results show that the arc detection algorithm has a high accuracy and a false detection rate of 0%. After data enhancement, it has generalisation of the SVM classification model under the condition of high power experiment.

show abstract

Binary classification model based on machine learning algorithm for the DC serial arc detection in electric vehicle battery system

Cited by 20 publications

References 26 publications

Wavelet entropy analysis and machine learning classification model of DC serial arc fault in electric vehicle power system

Wavelet entropy analysis and machine learning classification model of DC serial arc fault in electric vehicle power system

Different Domains Based Machine and Deep Learning Diagnosis for DC Series Arc Failure

Data‐enhanced machine recognition model of DC serial arc in electric vehicle power system

Contact Info

Product

Resources

About