Intelligent busbar protection scheme based on combination of support vector machine and S‐transform

Gil, Milad; Abdoos, Ali Akbar

doi:10.1049/iet-gtd.2016.1686

Cited by 38 publications

(52 citation statements)

References 28 publications

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“…One of the threats to the accuracy of transient‐based protective algorithms is noise. The accuracy of some of the busbar protection algorithms is affected by a noise of 30 dB and internal faults cannot be detected [8].…”

Section: Simulation and Resultsmentioning

confidence: 99%

“…However, CT is powerful in transforming high‐frequency current signals up to 50 kHz [7]. In other words, protective algorithms based on transient components using current and voltage waves and/or voltage signals are impractical and only and just only, current signal with the sampling frequency not larger than 100 kHz is the best input for algorithms that are based on transient components. Criterion #5: Rule‐based approach : The attempts for transient‐based busbar protection schemes, only represented by simulation results, can be divided into pattern‐recognition based [8, 9, 15] and rule‐based approaches [3–5, 16]. Inherently, the former approach requires massive simulations for training; while, generally, it cannot guarantee 100% accuracy in detecting faults of different conditions and discriminating internal and external faults. In Table 1, according to the aforementioned requirements, the comparison between some busbar protection algorithms that are based on transient components as well as the proposed algorithm has been shown.…”

Section: Introductionmentioning

confidence: 99%

“…Criterion #5: Rule‐based approach : The attempts for transient‐based busbar protection schemes, only represented by simulation results, can be divided into pattern‐recognition based [8, 9, 15] and rule‐based approaches [3–5, 16]. Inherently, the former approach requires massive simulations for training; while, generally, it cannot guarantee 100% accuracy in detecting faults of different conditions and discriminating internal and external faults.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fast transient‐based detection of busbar faults employing improved morphological gradient

Lashgari

Shahrtash

2020

IET Generation, Transmission & Distribution

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Section: Simulation and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fast transient‐based detection of busbar faults employing improved morphological gradient

Lashgari

Shahrtash

2020

IET Generation, Transmission & Distribution

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“…Conversely, the short circuit capacity of substation rises due to the expansion of interconnected power systems. Therefore, CT saturation has become an unavoidable event due to the growth of significant burden impedance in CT, remnant flux, and large primary currents due to a fault [3].…”

Section: Motivationmentioning

confidence: 99%

An Accurate CT Saturation Classification Using a Deep Learning Approach Based on Unsupervised Feature Extraction and Supervised Fine-Tuning Strategy

et al. 2017

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Current transformer (CT) saturation is one of the significant problems for protection engineers. If CT saturation is not tackled properly, it can cause a disastrous effect on the stability of the power system, and may even create a complete blackout. To cope with CT saturation properly, an accurate detection or classification should be preceded. Recently, deep learning (DL) methods have brought a subversive revolution in the field of artificial intelligence (AI). This paper presents a new DL classification method based on unsupervised feature extraction and supervised fine-tuning strategy to classify the saturated and unsaturated regions in case of CT saturation. In other words, if protection system is subjected to a CT saturation, proposed method will correctly classify the different levels of saturation with a high accuracy. Traditional AI methods are mostly based on supervised learning and rely heavily on human crafted features. This paper contributes to an unsupervised feature extraction, using autoencoders and deep neural networks (DNNs) to extract features automatically without prior knowledge of optimal features. To validate the effectiveness of proposed method, a variety of simulation tests are conducted, and classification results are analyzed using standard classification metrics. Simulation results confirm that proposed method classifies the different levels of CT saturation with a remarkable accuracy and has unique feature extraction capabilities. Lastly, we provided a potential future research direction to conclude this paper.

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“…However, only the traveling wave head information was utilized, and the criterion reliability was insufficient. [14] used a support vector machine (SVM) and an S transform to identify the fault zone, achieving highly accurate fault classification with diverse system parameters. In [15], a busbar protection scheme based on a relevance vector machine (RVM) was proposed.…”

Section: Introductionmentioning

confidence: 99%

New principle of busbar protection based on a fundamental frequency polarity comparison

Dong

Wang

2019

PLoS ONE

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To overcome the contradiction between speed and reliability in existing busbar protection schemes, a new busbar protection algorithm based on a polarity comparison of fundamental frequency currents is proposed. The algorithm extracts the fundamental frequency components of the fault reference current and the virtual current through a wavelet transform. The angle between the two currents is used to characterize the polarity relationship. The polarities of the virtual current and the reference current are the same when an internal fault occurs, and the angle will be small. The polarities of the two currents are opposite for an external fault, in which case the angle is larger. By analysing the variation characteristics of the angle between faults inside and outside busbar, a protection criterion is established, and the fault area is determined. In simulation results based on PSCAD/EMTDC, the algorithm can quickly and reliably identify the faults inside and outside the busbar area, and its performance is not affected by the initial fault angle, fault resistance, fault type or capacitor voltage transformer (CVT) transmission characteristics.

show abstract

Intelligent busbar protection scheme based on combination of support vector machine and S‐transform

Cited by 38 publications

References 28 publications

Fast transient‐based detection of busbar faults employing improved morphological gradient

Fast transient‐based detection of busbar faults employing improved morphological gradient

An Accurate CT Saturation Classification Using a Deep Learning Approach Based on Unsupervised Feature Extraction and Supervised Fine-Tuning Strategy

New principle of busbar protection based on a fundamental frequency polarity comparison

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