A review on fault classification methodologies in power transmission systems: Part—I

Prasad, Avagaddi; Edward, J. Belwin; Ravi, K.

doi:10.1016/j.jesit.2017.01.004

Cited by 86 publications

(45 citation statements)

References 16 publications

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“…Transmission lines against invading foreign objects is the most basic errand in the assurance of power system [23], [24]. Overhead line failures caused by invading foreign objects are relatively common anomalies, which can be caused by climatic conditions, human mistakes, flames and smoke, and construction machinery, etc [5], [25].…”

Section: Related Work a Image Online Monitoringmentioning

confidence: 99%

A Deep Learning Method to Detect Foreign Objects for Inspecting Power Transmission Lines

et al. 2020

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Image online monitoring technology has been widely used in transmission lines inspection, but the intelligent and efficient foreign object detection still has a gap with the ideal. In this paper, we propose a deep learning method to detect invading foreign objects for power transmission line inspection. Specifically, we design our network based on the regression strategy with oriented bounding boxes to accurately predict spatial location and orientation angle of foreign objects, as well as their categories in cluttered backgrounds. Moreover, an easy yet effective Scale Histogram Matching method is proposed to be applied to the publicly available dataset, allowing useful patterns to be exploited to detect tiny foreign objects during the pretraining procedure and boosting detection performance even with limited annotated samples. Besides, we construct an image dataset that contains common foreign objects in transmission line scenarios to evaluate proposed methods, on which experiment results show our full model achieves accuracy with 88.1% mean Average Precision (mAP). Additionally, the efficient and compact network structure allows our network to run in real-time, which provides possibilities for practical use. INDEX TERMS Image online monitoring technology, power transmission line inspection, deep learning, oriented bounding boxes, scale histogram matching.

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Section: Related Work a Image Online Monitoringmentioning

confidence: 99%

A Deep Learning Method to Detect Foreign Objects for Inspecting Power Transmission Lines

et al. 2020

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“…On the one hand, there are different reviews about DM applications, e.g., diagnosis in health [9][10][11], marketing [12], industrial [13,14], climatological [15], and financial [16] issues, among others. On the other hand, there are also reviews related to diagnosis methods for specific machines such as transformers [17][18][19], estimation strategies in electric vehicles [20], mathematical models used to study induction motors in defective conditions [21], or, in a more general sense, methodologies of fault classification in transmission systems [22,23] and distribution of energy [24]. There is also the work of Hare et al [25], where they present a study of modern diagnosis methods in smart micro grids.…”

Section: Introductionmentioning

confidence: 99%

Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review

et al. 2020

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Data mining is a technological and scientific field that, over the years, has been gaining more importance in many areas, attracting scientists, developers, and researchers around the world. The reason for this enthusiasm derives from the remarkable benefits of its usefulness, such as the exploitation of large databases and the use of the information extracted from them in an intelligent way through the analysis and discovery of knowledge. This document provides a review of the predictive data mining techniques used for the diagnosis and detection of faults in electric equipment, which constitutes the pillar of any industrialized country. Starting from the year 2000 to the present, a revision of the methods used in the tasks of classification and regression for the diagnosis of electric equipment is carried out. Current research on data mining techniques is also listed and discussed according to the results obtained by different authors.

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“…Hybrid methods are a combination of more than one of the prominent methods to produce new four techniques. Modern methods use others techniques such as support vector machines, genetic algorithms, decision trees etc as a tool to classify fault on transmission lines [5,6]. The wavelet approach is a numerical tool used for signal processing to select wavelet functions known as -mother wavelet‖.…”

Section: Introductionmentioning

confidence: 99%

Fault classification on transmission line using LSTM network

Omar

Osman

Ibrahim

et al. 2020

IJEECS

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Deep Learning has ignited great international attention in modern artificial intelligence techniques. The method has been widely applied in many power system applications and produced promising results. A few attempts have been made to classify fault on transmission lines using various deep learning methods. However, a type of deep learning called Long Short-Term Memory (LSTM) has not been reported in literature. Therefore, this paper presents fault classification on transmission line using LSTM network as a tool to classify different types of faults. In this study, a transmission line model with 400 kV and 100 km distance was modelled. Fault free and 10 types of fault signals are generated from the transmission line model. Fault signals are pre-processed by extracting post-fault current signals. Then, these signals are fed as input to the LSTM network and trained to classify 10 types of faults. The white Gaussian noise of level 20 dB and 30 dB signal to noise ratio (SNR) is also added to the fault current signals to evaluate the immunity of the proposed model. Simulation results show promising classification accuracy of 100%, 99.77% and 99.55% for ideal, 30 dB and 20 dB noise respectively. Results has been compared to four different methods which can be seen that the LSTM leading with the highest classification accuracy. In line with the purpose of the LSTM functions, it can be concluded that the method has a capability to classify fault signals with high accuracy.

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A review on fault classification methodologies in power transmission systems: Part—I

Cited by 86 publications

References 16 publications

A Deep Learning Method to Detect Foreign Objects for Inspecting Power Transmission Lines

A Deep Learning Method to Detect Foreign Objects for Inspecting Power Transmission Lines

Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review

Fault classification on transmission line using LSTM network

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