HIF detection in distribution networks based on Kullback–Leibler divergence

Nezamzadeh-Ejieh, Shiva; Sadeghkhani, Iman

doi:10.1049/iet-gtd.2019.0001

Cited by 29 publications

(13 citation statements)

References 31 publications

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“…As shown in Fig.7, the value of standardized autocorrelation length is between 0 and 1 by (4). The optimal length L is determined as 4574 through (5), which is shorter than the all data length. Moreover, it is 2160 less than the longest data 6743.…”

Section: B Effect Of Different Data Lengthmentioning

confidence: 99%

“…A method of extracting Transformed Components (TCs) online by Principal Component Analysis (PCA) and estimating the time-varying characteristics of the most sensitive part by Kernel Density Estimation (KDE) is proposed, which can be easily integrated with the data storage units of Rooftop Mounted Photovoltaic (RMPV) system [4]. Shiva et al uses KLD pair to quantify some characteristics of the current waveform into high-impedance fault (HIF) standard, thus proposes a time-domain high impedance fault detection algorithm based on monitoring substation current waveform [5]. Chen et al proposed a method based on KLD and independent component analysis (ICA) to solve the problem of initial fault in the electrical traction systems of a highspeed train.…”

Section: Introductionmentioning

confidence: 99%

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Optimized KLD-Based Fault Detection Method for Complex System Under Multi-Operation Conditions

Shi

Zhou

et al. 2020

IEEE Access

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With the increasing development of industrial technology, fault detection technology has been widely applied in many complex systems. Aiming at the problems of multi-operation conditions and the data length needed for fault detection using Kullback-Leibler Divergence (KLD), a novel fault detection method for complex systems based on optimized KLD under multi-operation conditions is proposed in this paper. In the first place, based on the historical data of the system, the complex operation conditions of the system are divided into several simple mutually exclusive operation conditions, and the division standard is established. In the next place, the optimal length can be quickly determined by autocorrelation analysis and applied to various operation conditions. After the next, in the training stage, the KLD values of all training data are calculated with the benchmark that is the initial optimal data length of each operation condition. And the maximum value is taken as the threshold for fault detection. Afterward, in the test phase, it starts by judging the type of operation condition that the data belongs to, then the corresponding KLD value is calculated, which is compared with the corresponding threshold, so as to determine whether the fault occurs. Eventually, this method is applied to the suspension system of the maglev train and respectively compared with the fault detection methods based on Euclidean distance or Mahalanobis distance. The results show that the proposed method possesses a low false alarm rate and high sensitivity.

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Section: B Effect Of Different Data Lengthmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimized KLD-Based Fault Detection Method for Complex System Under Multi-Operation Conditions

Shi

Zhou

et al. 2020

IEEE Access

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“…The most basic structure in a neural network is called a neuron. For the most common neural networks, the individual neurons between two adjacent layers are connected to each other, and each FIGURE 6 Work process with DNNs connection is given a weight. The whole neural network is composed of some neurons organized by layers.…”

Section: Deep Neural Networkmentioning

confidence: 99%

“…For each test sample, the DNNs output a result that a certain line is fault. When all 100 samples have been tested, the DP of each line can be given by referring to Equation (6). A total of 12 cases are tested and the results are shown in Table 4.…”

Section: Resistance Change Testmentioning

confidence: 99%

“…In [5], Dong and Shi identified the faulty line by wavelet transforming and highlighted the characteristics of the fault line by comparing the wavelet analysis results of the transient electrical signal. In some cases, it has excellent performance but a quite high sampling rate is needed and it affected by the moment when the fault occurs; Nezamzadeh-ejieh and Sadeghkhani studied a method for comparing transient current waveforms based on Kullback-Leibler divergence to identify high-impedance fault (HIF) [6]. Apparently, the waveforms are also required to be recorded for analysis so that a high sampling frequency is needed.…”

Section: Introductionmentioning

confidence: 99%

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Identifying single‐phase to ground fault based on line multi‐dimensional data in cyber‐physical distribution system

Chen

Yang

et al. 2020

IET Generation Trans & Dist

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Single‐phase to ground fault is the most common line fault in power distribution systems, which may result in an electrical fire and personal safety problems. Particularly, it is more difficult to identify the fault occurring in the system in which the neutral point is grounded via Petersen coil. This paper proposes a new scheme based on line multi‐dimensional data in cyber‐physical distribution system, where line multi‐dimensional data is used to extract the fault characteristics and thus to identify the faulty line. First, the circuit is analyzed and the difference‐like algorithm is used to extract the current signal difference value from the line multi‐dimensional data as the feature quantity. In order to reduce the impact of the measurement error on the result, the authors employ the deep neural networks to process the line multi‐dimensional data, and demonstrate that this method can effectively resist the error interference. A simulation based on a previously studied model has been carried out and the results show the superiority compared to the fifth harmonic identification method. In addition, the test results obtained in the Dynamic Simulation Laboratory of Huazhong University of Science and Technology also demonstrate the practicality of the scheme.

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LSTM-based low-impedance fault and high-impedance fault detection and classification

Bhatnagar,

Yadav,

Swetapadma

et al. 2024

Electr Eng

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HIF detection in distribution networks based on Kullback–Leibler divergence

Cited by 29 publications

References 31 publications

Optimized KLD-Based Fault Detection Method for Complex System Under Multi-Operation Conditions

Optimized KLD-Based Fault Detection Method for Complex System Under Multi-Operation Conditions

Identifying single‐phase to ground fault based on line multi‐dimensional data in cyber‐physical distribution system

LSTM-based low-impedance fault and high-impedance fault detection and classification

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