Application of machine learning methods in fault detection and classification of power transmission lines: a survey

Shakiba, Fatemeh Mohammadi; Azizi, S. Mohsen; Zhou, MengChu; Abusorrah, Abdullah

doi:10.1007/s10462-022-10296-0

Cited by 62 publications

(20 citation statements)

References 142 publications

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“…Shakiba et al [35] discuss relevant methods proposed since 2015, analyzing the advantages and disadvantages of machine learning techniques for HIF detection. They also identify notable issues in the field of HIF detection, such as voltage amplitude variations and phase changes in power systems, the scarcity of real-world faults for training, noise, and false detection of HIF.…”

Section: Related Workmentioning

confidence: 99%

Artificial Neural Networks: Modeling and Comparison to Detect High Impedance Faults

Diefenthäler,

Sausen,

De Campos

et al. 2023

IEEE Access

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High impedance faults constitute one of the biggest challenges in electrical power systems. In overhead distribution systems, faults are caused by tree branches that touch the electrical grid or by the rupture of energized conductors on low conductivity soils. They are also known as low-current faults, which are not detected by conventional protection systems, compromising the quality of the power supply and causing hazardous risks to the electrical system. This paper aims to address the problem of high impedance faults detection using Artificial Neural Networks: two Multi Layer Perceptron networks, being one Neural Pattern Recognition and another Neural Fitting, and a Convolutional Neural Network. The neural networks are trained and analyzed in scenarios based on a medium-voltage distribution grid model, located in the Basque Country, Spain. The network topologies are implemented, repeatedly trained considering multiple architectures, and validated in other scenarios with different location, time, and duration of the fault using the Matlab software. After, the criteria of accuracy, reliability, security, safety and sensitivity are evaluated. At last, a comparative analysis between them is carried out, and from the results obtained, a superior performance of the Convolutional Neural Network in compared to the Multi Layer Perceptron networks is observed.

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Section: Related Workmentioning

confidence: 99%

Artificial Neural Networks: Modeling and Comparison to Detect High Impedance Faults

Diefenthäler,

Sausen,

De Campos

et al. 2023

IEEE Access

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“…This paper aims to provide an overview of the current state of video-based defect detection techniques for power transmission and distribution equipment. It will explore the challenges faced by traditional inspection methods, investigate the potential benefits of video analysis in defect detection, and discuss various methodologies and algorithms employed in this field [9].…”

Section: Introductionmentioning

confidence: 99%

External defect detection technology and application status of transmission and transformation equipment based on video images

Hao,

Lv,

Liu

et al. 2024

J. Phys.: Conf. Ser.

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Power transmission and distribution equipment plays a critical role in supplying electricity to consumers. However, these assets are susceptible to external defects, such as corrosion, mechanical damage, and wear, which can lead to failures and disruptions in the electrical grid. Traditional inspection methods for detecting these defects often rely on manual inspections, which are time-consuming, costly, and subjective. To overcome these limitations, this paper explores the current state of video image-based external defect detection techniques for power transmission and distribution equipment. This makes up for the deficiencies of conventional approaches to inspecting and maintaining power transmission and transformation equipment by decreasing the waste of human resources and increasing the frequency and efficiency of intelligent operation and maintenance of power systems. This work investigates a completely convolutional block detection-based defect identification method to address the issue of defect recognition. The fully convolutional neural network is enhanced with the concept of block detection thanks to this approach. The local discrimination mechanism may be realized, and the drawbacks of the conventional block detection receptive field are avoided in the process. This approach offers improved generalization and fault identification over the original ResNet image classification system.

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“…The deregularization of the power sector has forced the utilities to provide uninterruptable power to their customers and resolve power quality concerns promptly 2 . The power companies are also looking at reducing the downtime of systems in case of any emergency or abnormal situation 3 . The practice of the power operators should be to minimize the fault clearance time so that the system can be brought back into its normal operating state within the minimum possible time 4 …”

Section: Introductionmentioning

confidence: 99%

“…abnormal situation. 3 The practice of the power operators should be to minimize the fault clearance time so that the system can be brought back into its normal operating state within the minimum possible time. 4 In case of any abnormal situation in transmission lines, detection and classification of faults must be as fast as possible so that protective devices can isolate the faulty section to safeguard and protect the power system from damages, such as outages, thermal loading, voltage, angular instability, and so forth.…”

mentioning

confidence: 99%

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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Application of machine learning methods in fault detection and classification of power transmission lines: a survey

Cited by 62 publications

References 142 publications

Artificial Neural Networks: Modeling and Comparison to Detect High Impedance Faults

Artificial Neural Networks: Modeling and Comparison to Detect High Impedance Faults

External defect detection technology and application status of transmission and transformation equipment based on video images

Fault detection through discrete wavelet transform in overhead power transmission lines

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