Fourier Transform and Short-Time Fourier Transform Decomposition for Photovoltaic Arc Fault Detection

Balamurugan, R.; Al-Janahi, Fatima; Bouhali, O.; Shukri, Sawsan; Abdulmawjood, Kais; Balog, Robert S.

doi:10.1109/pvsc45281.2020.9300455

Cited by 13 publications

(5 citation statements)

References 5 publications

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“…Extending from traditional Fourier Transform methods, the Short-Time Fourier Transform provides more focus fault detection capability. Using Short-Time Fourier Transform techniques, Balamurugan et al [13] transformed voltage and current waveforms from a photovoltaic electric arc test bed and identified faults in frequency domain. Their frequency-based fault indicators were able to detect fault conditions repeatedly.…”

Section: Frequency Domain Characterised Indicatorsmentioning

confidence: 99%

Sum Standard Deviation of Frequency – A Context Independent Machine Condition Trend Indicator

Feng,

2023

Digit. Manuf. Technol.

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In Industry 4.0, multiple research subjects have been involved in processing enormous information generated during the manufacturing process. Meanwhile, they all pursue the same goal, that is, to maintain the manufacturing process at good operating state. Hence, many researchers are focusing their researches on continuous health monitoring using digital signal processing techniques, in particular, most researches are focused on recognition of a fault condition only. However, by the time when a fault is recognised, at least one reject has been produced, or an impact to the system's operation has occurred. In addition, many algorithms are designed specifically for certain class of applications. This paper proposes a new system health condition indicator, Sum Standard Deviation of Frequency (SSDF), which is computed from a new computational process that segments raw data streams into time segments and the segments are synchrosqueezed continuous wavelet transformed. As long as sufficient data is available, SSDF shows distinct consecutive regions for "normal", "marginal" and "abnormal" machine conditions. Actions can then be taken while the machine is in "marginal" conditions in which the manufacturing quality is still acceptable. SSDF does not link to any application context information of the raw signal data stream hence making it context independent.

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Section: Frequency Domain Characterised Indicatorsmentioning

confidence: 99%

Sum Standard Deviation of Frequency – A Context Independent Machine Condition Trend Indicator

Feng,

2023

Digit. Manuf. Technol.

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“…In the case of the FFT, it is impossible to analyze non-stationary signals as these signals have a complex structure with a different set of frequencies, which allows additional spectra to occur in the spectral analysis of the FFT. Furthermore, it is necessary to use a window weighting function f(σ) for the waveform to compensate for spectral dissipation, reducing the loss of information [51][52][53][54].…”

Section: Fast Fourier Transformmentioning

confidence: 99%

Analysis of possible faults and diagnostic methods of the Cartesian industrial robot

Autsou¹,

Kudelina²,

Rassõlkin³

et al. 2022

PEAS

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The paper discusses problems that occur in robotic arm control. The specific problems arise due to the wear of various types of gears (in the presented case, belt gear and worm gear). It is important to note that such errors need to be diagnosed in time, and the way of their elimination has to be determined, which should be resource-intensive and cost-effective. This article describes the basic types of robotic manipulators (robotic arm, telpher and Cartesian), presenting a review and study of the possibilities of errors in the movement of a robot, adjustment of a mechanical system, and determination of a strategy for solving the emerging problems. A comparison between various types of gear faults is also provided. Different ways of diagnosing faults are discussed, based on the advantages and disadvantages of the methods. The main objective of this study is to provide a complete overview of the mechanical areas where disturbances occur, their diagnosing, and methods of their elimination.

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“…Research has explored methods for arc noise analysis and detection in the frequency domain, leveraging the fast Fourier transform (FFT) technique. In [14], a comparison of FFT waveforms in terms of arc presence is presented, while ref. [15] demonstrates the application of feature extraction to arc detection.…”

Section: Introductionmentioning

confidence: 99%

Empirical Filtering-Based Artificial Intelligence Learning Diagnosis of Series DC Arc Faults in Time Domains

Dang,

Kwak,

Choi

2023

Machines

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Direct current (DC) networks play a pivotal role in the growing integration of renewable energy sources. However, the occurrence of DC arc faults can introduce disruptions and pose fire hazards within these networks. In order to ensure both safety and optimal functionality, it becomes imperative to comprehend the characteristics of DC arc faults and implement a dependable detection system. This paper introduces an innovative arc fault detection algorithm that leverages current filtering based on the empirical rule in conjunction with intelligent machine learning techniques. The core of this approach involves the sampling and subsequent filtration of current using the empirical rule. This filtering process effectively amplifies the distinctions between normal and arcing states, thereby enhancing the overall performance of the intelligent learning techniques integrated into the system. Furthermore, this proposed diagnosis scheme requires only the signal from the current sensor, which reduces the complexity of the diagnosis scheme. The results obtained from the detection process serve to affirm the effectiveness and reliability of the proposed DC arc fault diagnosis scheme.

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Fourier Transform and Short-Time Fourier Transform Decomposition for Photovoltaic Arc Fault Detection

Cited by 13 publications

References 5 publications

Sum Standard Deviation of Frequency – A Context Independent Machine Condition Trend Indicator

Sum Standard Deviation of Frequency – A Context Independent Machine Condition Trend Indicator

Analysis of possible faults and diagnostic methods of the Cartesian industrial robot

Empirical Filtering-Based Artificial Intelligence Learning Diagnosis of Series DC Arc Faults in Time Domains

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