An improved protection strategy based on PCC-SVM algorithm for identification of high impedance arcing fault in smart microgrids in the presence of distributed generation

Eslami, Mostafa; Jannati, Mohsen; Tabatabaei, S. Sepehr

doi:10.1016/j.measurement.2021.109149

Cited by 16 publications

(8 citation statements)

References 42 publications

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“…The effectiveness of any classifier to discriminate typical faults resulting from high impedance faults depends on the efficiency of the feature extraction process. Feature extraction of high impedance faults is not an easy task since current magnitudes may present small changes that can be interpreted as load variations [21]. As a consequence, HIFs require robust algorithms able to distinguish them from other fault types.…”

Section: Classification Approach For High Impedance Faults a Feature ...mentioning

confidence: 99%

“…Another approach based on time-frequency analysis and a support vector machine (SVM) classifier was proposed by [20]. A similar application of SVM is reported in [21], which is combined with Principal Component Analysis (PCA) to cope with the detection and classification of HIFs.…”

mentioning

confidence: 99%

“…ploys only two features such as energy and standard deviation whose accuracy was 98.3298 %. In[21] a classification…”

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confidence: 99%

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Hermite Transform Based Algorithm for Detection and Classification of High Impedance Faults

et al. 2022

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This work proposes a new algorithm to classify high impedance faults (HIFs) in distribution systems. HIFs can be represented by small current magnitudes with non-linear variations, which complicate their detection in distribution grids. The proposed method uses the Hermite transform (HT) as a signal processing technique that offers several advantages, one of the more important being the ability to analyze the signal at multiple resolution levels and at different frequency bands thanks to their filter functions based on Gaussian derivatives. The Hermite coefficients are used to extract signal features that allow efficient identification of the transient behaviour related to HIFs and other typical faults. In this sense, a multichannel approach based on the Hermite transform is proposed to classify different types of faults and HIFs. This analysis is carried out in a distribution network considering photovoltaic (PV) systems considering three different classifiers, which are also used to compare our results with the discrete wavelet transform (DWT) as signal decomposition model; the comparison suggests that our proposal presents better performance discriminating HIFs from typical faults.

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Section: Classification Approach For High Impedance Faults a Feature ...mentioning

confidence: 99%

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Hermite Transform Based Algorithm for Detection and Classification of High Impedance Faults

et al. 2022

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“…Therefore, Kazemi et al developed the extended Kalman filter-based SVM model to classify the three-phase residual currents in the primary winding of a transformer, where three residual signals are defined as the discrepancies between the measured and estimated three-phase currents 26 . ESlami et al adopted SVM for identifying high impedance arcing failures in a distributed generation integrated microgrid where principal component analysis and the Pearson correlation coefficient technique were used to scale down and select features, respectively 27 . The Ref 28 offers a k-means-based classification algorithm for finding abnormalities in the residual current of a solar system.…”

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confidence: 99%

An energy and leakage current monitoring system for abnormality detection in electrical appliances

Alam

Shahjalal

Rahman

et al. 2022

Sci Rep

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Unsafe electrical appliances can be hazardous to humans and can cause electrical fires if not monitored, analyzed, and controlled. The purpose of this study is to monitor the system’s condition, including the electrical properties of the appliances, and to diagnose fault conditions without deploying sensors on individual appliances and analyzing individual sensor data. Using historical data and an acceptable range of normal and leakage currents, we proposed a hybrid model based on multiclass support vector machines (MSVM) integrated with a rule-based classifier (RBC) to determine the changes in leakage currents caused by installed devices at a certain moment. For this, we developed a sensor-based monitoring device with long-range communication to store real-time data in a cloud database. In the modeling process, RBC algorithm is used to diagnose the constructed device fault and overcurrent fault where MSVM is applied for detecting leakage current fault. To conduct an operational field test, the developed device was integrated into some houses. The results demonstrate the effectiveness of the proposed system in terms of electrical safety monitoring and detection. All the collected data were stored in a structured database that could be remotely accessed through the Internet.

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“…In [8], authors applied fuzzy system to design a protection approach for detection and classification of the short circuit fault incident on transmission line used for integration of wind power plant (WPP) to network of utility grid. A Pearson Correlation Coefficient (PCC) and support vector machine (SVM) based algorithm to detect high impedance arcing faults incident on micro-grids interfaced with DGs is introduced in [9]. Protection scheme is fast and detect faults with high accuracy.…”

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confidence: 99%

Fusion of Signal Processing Techniques to Design Current and Voltage Features Based Protection Scheme for Utility Grid With Renewable Energy Penetration

Gupta

Pachar

Mahela³

et al. 2022

IEEE Access

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This paper used fusion of Hilbert transform (HT), Stockwell transform (ST) and Alienation coefficient to design a protection scheme for utility network with high concentration of renewable energy (RE) generation. A hybrid fault detection index (HFDI) is designed which detects the fault events utilizing the features extracted from both current and voltage signals. Peak magnitude of HFDI is compared with a threshold magnitude (HTM) to detect fault events and discriminate such events from operational events. Faults are classified based on faulty phase numbers and a hybrid ground fault index (HGFI). Zero sequence voltage and zero sequence current are processed by application of ST to compute HGFI which effectively detects presence of ground during fault event. Performance of protection scheme is validated using utility grid of IEEE-13 bus test system where 50% RE penetration is used. Further, performance is also tested to detect faults incident on a practical utility network with RE penetration of 57%. Performance of algorithm is evaluated to recognize different fault events and operational events. Protection scheme is effectively tested to detect faults with high noise levels of 20dB SNR (signal to noise ratio). Proposed protection scheme perform better compared to Alienation coefficient based protection scheme reported in literature.

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An improved protection strategy based on PCC-SVM algorithm for identification of high impedance arcing fault in smart microgrids in the presence of distributed generation

Cited by 16 publications

References 42 publications

Hermite Transform Based Algorithm for Detection and Classification of High Impedance Faults

Hermite Transform Based Algorithm for Detection and Classification of High Impedance Faults

An energy and leakage current monitoring system for abnormality detection in electrical appliances

Fusion of Signal Processing Techniques to Design Current and Voltage Features Based Protection Scheme for Utility Grid With Renewable Energy Penetration

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