Machine learning-based system for fault detection on anchor rods of cable-stayed power transmission towers

Coutinho, M. S.; Novo, Lauro R. G. S. L.; Melo, M. T. de; Medeiros, L.H.A. de; Barbosa, Douglas; Alves, Marcelo; Tarrago, V. L.; Santos, R. G. M. dos; Neto, H.B.T.D. Lott; Gama, P. H. R. P.

doi:10.1016/j.epsr.2021.107106

Cited by 16 publications

(5 citation statements)

References 18 publications

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“…To achieve this, a stepped-frequency continuous wave (SFCW) radar is ideal [42], as it allows very low noise figures for the receiver owing to its instantaneous narrow bandwidth as well as the high average power that can be transmitted during long temporal pulses. These advantages, along with the ability to synthesize very large bandwidths (in some cases exceeding 40 GHz), had already brought such systems for applications in transmission line fault detection [43], ground penetrating [3], through the wall [32,33], and other applications where cost is not a significant factor. In such cases, VNAs can find use, yet their relatively high cost, along with a lack of clear understanding of how to apply them to dynamic scenarios, keeps them from being employed in broader applications.…”

Section: Methods-extracting Raw Data From the Frequency Response Of S...mentioning

confidence: 99%

Universal Software Only Radar with All Waveforms Simultaneously on a Single Platform

Kozlov,

Kharchevskii,

Rebenshtok

et al. 2024

Remote Sensing

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While software-defined radars can switch their transmitted waveform on the go, they cannot transmit all waveforms at the same time, meaning they must balance the advantages and drawbacks of each configuration. Here, we propose theoretically and demonstrate experimentally the universal radar, which can apply the desired waveform in the post-processing stage after the physical measurement has been performed. This method also allows using a single measurement of a scene to design and test any other radar in complex scenarios without having to take it to the field. The method is based on post-processing the frequency response measured by a synthetically broadband stepped-frequency continuous wave radar, such as a vector network analyzer. An algorithm for overcoming distortions due to moving targets is derived as well. This approach not only provides an ultra-wideband software-only defined radar, but it also enables the acquired data from any measured site to be used for the design and analysis of almost any other future radar system, significantly cutting the time and cost of new developments. The method suggests the creation of radar raw data repositories that can be shared across diversely different radar platforms.

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Section: Methods-extracting Raw Data From the Frequency Response Of S...mentioning

confidence: 99%

Universal Software Only Radar with All Waveforms Simultaneously on a Single Platform

Kozlov,

Kharchevskii,

Rebenshtok

et al. 2024

Remote Sensing

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show abstract

“…To enhance the performance of these classifiers, hyperparameter tuning and data balancing using the Synthetic Minority Oversampling Technique (SMOTE) were evaluated. Medeiros et al [11] developed a field application system for detecting structural faults on anchor rods using frequency domain reflectometry analysis. The system utilizes ML techniques to classify the measured signals as normal or faulty, achieving an accuracy greater than 98%.…”

Section: Machinementioning

confidence: 99%

Optimal PMU Placement for Fault Classification and Localization Using Enhanced Feature Selection in Machine Learning Algorithms

Faza,

Al-Mousa,

Alqudah

2024

International Journal of Energy Research

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Machine learning (ML) algorithms are increasingly used in power systems applications. One important application is the classification and localization of various types of transmission line faults. Using voltage and current measurements from phasor measurement units (PMUs), a number of useful features can be extracted, which can form the basis of a ML-based prediction of the fault type, line, and distance on the line. This paper proposes a technique to find the optimal number and placement of PMUs by performing thorough feature selection. The features are selected to maximize the accuracy of the ML classification and regression algorithms. The results show that for the IEEE 14 bus system, the use of only five PMUs is sufficient to obtain high levels of accuracy. For example, a testing accuracy of 99.0% and 97.1% can be achieved for the fault type and fault line location, respectively. As for the fault distance along the line, the testing MAE of 3.1% can be obtained along with an R2 score of 94.4%. Adding more PMUs does not provide any additional value in terms of accuracy.

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“…Electricity load was forecasted by using the recurrent extreme learning machine model in [2]. ML algorithms are also used for the detection of unwanted events in a network, e.g., detection of faults [3] or power quality disturbances [4]. The other potential of ML algorithms is used in the detection of energy thefts in smart distribution networks using end-users' consumption patterns [5].…”

Section: Introductionmentioning

confidence: 99%

On the Value of Distribution Network Topology Information in the Identification of End-user Phase Consumption: A Graph Neural Network Approach

Terezija¹,

Matijašević²,

Antić³

et al. 2023

Preprint

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Machine learning-based system for fault detection on anchor rods of cable-stayed power transmission towers

Cited by 16 publications

References 18 publications

Universal Software Only Radar with All Waveforms Simultaneously on a Single Platform

Universal Software Only Radar with All Waveforms Simultaneously on a Single Platform

Optimal PMU Placement for Fault Classification and Localization Using Enhanced Feature Selection in Machine Learning Algorithms

On the Value of Distribution Network Topology Information in the Identification of End-user Phase Consumption: A Graph Neural Network Approach

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