Kalman filter based incipient fault detection method for underground cables

Ghanbari, Teymoor

doi:10.1049/iet-gtd.2015.0040

Cited by 44 publications

(26 citation statements)

References 21 publications

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“…However, the results show that this method is less robust and leads to a higher false detection rate. Zhang, W. et al [8] detect the fault signal and calculate a new signal by using a Kalman filter to determine whether there is an incipient results show that this method is less robust and leads to a higher false detection rate. Zhang, W. et al [8] detect the fault signal and calculate a new signal by using a Kalman filter to determine whether there is an incipient fault.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang, W. et al [8] detect the fault signal and calculate a new signal by using a Kalman filter to determine whether there is an incipient results show that this method is less robust and leads to a higher false detection rate. Zhang, W. et al [8] detect the fault signal and calculate a new signal by using a Kalman filter to determine whether there is an incipient fault. In [9], it is assumed that the incipient cable fault is an arc fault, and the harmonic distortion rate of the fault voltage is calculated to determine whether an incipient fault occurs by comparing the distortion rate to the reference threshold.…”

Section: Introductionmentioning

confidence: 99%

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Cable Incipient Fault Identification with a Sparse Autoencoder and a Deep Belief Network

et al. 2019

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Incipient faults in power cables are a serious threat to power safety and are difficult to accurately identify. The traditional pattern recognition method based on feature extraction and feature selection has strong subjectivity. If the key feature information cannot be extracted accurately, the recognition accuracy will directly decrease. To accurately identify incipient faults in power cables, this paper combines a sparse autoencoder and a deep belief network to form a deep neural network, which relies on the powerful learning ability of the neural network to classify and identify various cable fault signals, without requiring preprocessing operations for the fault signals. The experimental results demonstrate that the proposed approach can effectively identify cable incipient faults from other disturbances with a similar overcurrent phenomenon and has a higher recognition accuracy and reliability than the traditional pattern recognition method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cable Incipient Fault Identification with a Sparse Autoencoder and a Deep Belief Network

et al. 2019

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“…23 Here, Kalman filter (KF) is employed instead of Wavelet transform for detection of series arc fault. 27,28 This paper introduces a simple decision-making approach to detect DC series arc faults in PV systems based on KF harmonic decomposition. KF for detection of various abnormal conditions in power systems has been reported in literature.…”

mentioning

confidence: 99%

“…KF for detection of various abnormal conditions in power systems has been reported in literature. [23][24][25][26][27] Some of them are based on comparison of the estimated parameters with the corresponding measured ones. [24][25][26] Some others are based on estimation of the harmonic content of the analyzed signals.…”

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confidence: 99%

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Kalman filter–based approach for detection of series arc fault in photovoltaic systems

Ahmadi

Samet

Ghanbari

2019

Int Trans Electr Energ Syst

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Summary Series arc fault is one of the main failures of photovoltaic (PV) systems that should be detected as soon as possible after occurrence. Electrical shock hazard and fire in PV systems are the main consequences of delayed series arc fault detection. In this paper, an approach based on analyzing high‐frequency components of voltage signal derived by Kalman filter is presented to detect series arc fault occurrence. A criterion is defined using energy variations of the estimated harmonics for this purpose. The performance of the proposed method is evaluated by plenty of simulations and experiments in different conditions. These cases include different arc lengths, electrodes movements, temporary shadings, and different types of noise. The results confirm that the proposed technique is efficient from speed response, reliability, and accuracy points of view.

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