On the protection of power system: Transmission line fault analysis based on an optimal machine learning approach

Uddin, Md. Sihab; Hossain, Md. Zahid; Fahim, Shahriar Rahman; Sarker, Subrata; Bhuiyan, Erphan A.; Muyeen, S. M.; Das, Sajal K.

doi:10.1016/j.egyr.2022.07.163

Cited by 16 publications

(5 citation statements)

References 38 publications

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“…[62,63] Hyperparameter tuning of the models was performed using root mean square error (RMSE) as a criterion with fivefold cross-validation and grid search. [64] The models were subsequently fitted with the best parameters and used to predict the test set (see details in Notes S3, Supporting Information). The code for the analysis is available in the form of Jupiter notebooks on the project's GitHub page.…”

Section: Figure 10mentioning

confidence: 99%

Data‐Driven Tunnel Oxide Passivated Contact Solar Cell Performance Analysis Using Machine Learning

Zhou,

Jacobsson,

Wang

et al. 2024

Advanced Materials

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Tunnel oxide passivated contacts (TOPCon) have recently gained interest as a way to increase the energy conversion efficiency of silicon solar cells, and the International Technology Roadmap of Photovoltaics forecasts TOPCon to become an important technology despite a few remaining challenges. To review the recent development of TOPCon cells, we have compiled a dataset of all device data found in the current literature, which sums up to 405 devices from 131 papers. This may seem like a surprisingly small number of cells given the recent interest in the TOPCon architecture, but it illustrates a problem of data dissemination in the field. Notwithstanding the limited number of cells, there is a great diversity in cell manufacturing procedures, and we observe a gradual increase in performance indicating that the field has not yet converged on a set of best practices. By analyzing the data using statistical methods and machine learning (ML) algorithms, we were able to reinforces some commonly held hypotheses related to the performance differences between different device architectures. We also identify a few more unintuitive feature combinations that would be of interest for further experimentally studies. This work also aims to inspire improvements in data management and dissemination within the TOPCon community, which would further increase the value of statistical analysis like this as well as enable a larger part of the ML toolbox to be used.This article is protected by copyright. All rights reserved

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Section: Figure 10mentioning

confidence: 99%

Data‐Driven Tunnel Oxide Passivated Contact Solar Cell Performance Analysis Using Machine Learning

Zhou,

Jacobsson,

Wang

et al. 2024

Advanced Materials

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“…13 This technique is very helpful and simple in nature if the signal is static in nature. 14 Conveying accurate information can be challenging for transient or nonstationary signals because certain features may be lost during the conversion from frequency domain to time domain. 15 Expression of FT for signal x(t) is as under:…”

Section: Feature Extraction Techniques Used For Fault Detection In Tr...mentioning

confidence: 99%

“…Various tools used and reported in the past literature for feature extraction from the fault signal with their respective merits and limitations are presented: Fourier Transform (FT)FT actually breaks the input signal into smaller frequencies of different sinusoids 13 . This technique is very helpful and simple in nature if the signal is static in nature 14 . Conveying accurate information can be challenging for transient or nonstationary signals because certain features may be lost during the conversion from frequency domain to time domain 15 .…”

Section: Literature Review and Research Gap Analysismentioning

confidence: 99%

“…FT actually breaks the input signal into smaller frequencies of different sinusoids 13 . This technique is very helpful and simple in nature if the signal is static in nature 14 . Conveying accurate information can be challenging for transient or nonstationary signals because certain features may be lost during the conversion from frequency domain to time domain 15 .…”

Section: Literature Review and Research Gap Analysismentioning

confidence: 99%

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Fault detection through discrete wavelet transform in overhead power transmission lines

Ahmed,

Hashmani,

Khokhar

et al. 2023

Energy Science & Engineering

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Transmission lines are a very important and vulnerable part of the power system. Power supply to the consumers depends on the fault‐free status of transmission lines. If the normal working condition of the power system is disturbed due to faults, the persisting fault of long duration results in financial and economic losses. The fault analysis has an important association with the selection of protective devices and reliability assessment of high‐voltage transmission lines. It is imperative to devise a suitable feature extraction tool for accurate fault detection and classification in transmission lines. Several feature extraction techniques have been used in the past but due to their limitations, that is, for use in stationary signals, limited space in localizing nonstationary signals, and less robustness in case of variations in normal operation conditions. Not suitable for real‐time applications and large calculation time and memory requirements. This research presents a discrete wavelet transform (DWT)‐based novel fault detection technique at different parameters, that is, fault inception and fault resistance with proper selection of mother wavelet. In this study, the feasibility of DWT using MATLAB software has been investigated. It has been concluded from the simulated data that wavelet transform together with an effective classification algorithm can be implemented as an effective tool for real‐time monitoring and accurate fault detection and classification in the transmission lines.

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“…Liu Chuanyang et al [3] comprehensively discussed the research progress of deep learning-based detection methods for power transmission line inspection images collected by drones over the past decade. References [4][5] also utilized powerful neural network models in deep learning for direct fault diagnosis and identification training of infrared images. The major limitation of this method lies in its dependence on the quality and quantity of training data.…”

Section: Introductionmentioning

confidence: 99%

Recognition and Extraction of Power Transmission Lines Based on Infrared Image Processing for Line-following Robots

Zhang,

Gao

2023

AJST

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To further improve the real-time performance and accuracy of power transmission line maintenance, this paper primarily focuses on the preliminary line recognition and extraction method based on thermal image processing of infrared images collected by line-following robots for thermal fault detection. Firstly, filtering and noise reduction techniques along with enhanced image processing are applied to preprocess the collected infrared images. This effectively addresses the noise and interference from background objects, which can affect the extraction of overheated areas on the lines, while also reducing the computational memory required for subsequent image processing. Subsequently, an improved Canny edge detection algorithm is employed to extract the edges of foreground objects in the images. Additionally, a region-growing algorithm combined with the geometric features of the lines is employed to filter out unwanted thermal sources, enabling the accurate segmentation and extraction of power transmission lines. This forms a solid foundation for subsequent detection and identification of abnormal hotspots on the extracted lines, and holds significance for the inspection and maintenance of existing thermal faults and potential hotspots in power transmission lines.

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On the protection of power system: Transmission line fault analysis based on an optimal machine learning approach

Cited by 16 publications

References 38 publications

Data‐Driven Tunnel Oxide Passivated Contact Solar Cell Performance Analysis Using Machine Learning

Data‐Driven Tunnel Oxide Passivated Contact Solar Cell Performance Analysis Using Machine Learning

Fault detection through discrete wavelet transform in overhead power transmission lines

Recognition and Extraction of Power Transmission Lines Based on Infrared Image Processing for Line-following Robots

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