Classification and localization of transmission line faults using curve fitting technique with Principal component analysis features

Mukherjee, Alok; Kundu, Palash Kumar; Das, Arabinda

doi:10.1007/s00202-021-01285-7

Cited by 12 publications

(7 citation statements)

References 36 publications

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“…The singular and abrupt points contained in the output voltage signal of the AC substation are important information to describe the fault state. The time-frequency and frequent-domain information of the signal can be obtained by means of wavelet decomposition to quantify the local characteristic value of the voltage signal [8]. The singularity of the output voltage signal of an AC substation can be obtained by the position of the maximum value of the wavelet transform coefficient on the low highfrequency coefficient.…”

Section: Ac Substation Output Voltage Fault Detectionmentioning

confidence: 99%

Research on the accurate fault location of AC substations based on switching action logic

Xiao,

Meng,

Zou

et al. 2023

J. Phys.: Conf. Ser.

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AC substation systems contain multiple devices and components, which affect each other, resulting in increased system complexity and difficulty in locating power faults. However, the complexity of the system increases the difficulty of analysis and judgment, and puts forward higher requirements for the accuracy of algorithms and logic. Therefore, an accurate fault location method for AC substations based on switching action logic is proposed. Taking the YnD11 connection mode as an example, the operation model of the phase selection switch of the isolation switch is constructed, and the operation logic of the phase selection switch of the isolation switch is obtained. The phase amplitude and phase relationship of the three-phase isolation switch are analysed, and the appropriate switching strategy is selected to reduce the influence of electromagnetic coupling. Based on this, the time-frequency and frequency-domain information of the power signal is obtained by the wavelet decomposition method, and the local characteristic value of voltage signal is quantified. The fault location of the filtered the voltage signal is completed by the wavelet transform mode maximum method. The experimental results show that the no-load current has good linearity and almost no harmonics, and the results of the disturbance location are closer to the actual situation.

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Section: Ac Substation Output Voltage Fault Detectionmentioning

confidence: 99%

Research on the accurate fault location of AC substations based on switching action logic

Xiao,

Meng,

Zou

et al. 2023

J. Phys.: Conf. Ser.

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“…This facilitates the speedy elimination of the fault-causing component and the return of normal power flow. Additionally, the presence of OHTL noise complicates the operation by injecting harmonics into the system [10].…”

Section: Introductionmentioning

confidence: 99%

Support Vector Machine Parameters Optimization for 500 kV Long OHTL Fault Diagnosis

et al. 2023

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Faults can seriously damage high-voltage (HV) power systems, particularly if they occur on the long overhead transmission line (OHTL) that connects the nuclear power plant (NPP) to the electrical grid. Finding OHTL problems quickly and accurately is essential for the economy, safety, and dependability of the HV power systems. It is essential to pinpoint the problematic phase to avoid unneeded power outages. Thus, one of the most crucial research challenges is now how to identify, classify, and locate OHTL faults. In this study, transient current with high frequency oscillations that arise immediately after a defect at the sending end is investigated in a single-circuit, single-side fed Egyptian 500-kV HV long OHTL. Asymmetric and symmetric faults and locations are also represented in the Alternative Transients Program-Electro Magnetic Transients Program (ATP/EMTP) simulation model under varying fault resistance and inception angles. The proposed solution in this paper is an Optimized Support Vector Machine (OSVM), whose characteristics are optimized via a mutant particle swarm optimization (MPSO) method to detect 500 kV long OHTL faults. The localizer model is also built for practical applications, including power system noise contaminating fault signals. The findings prove that the suggested approach locates the fault in 0.012 seconds from the start of the event, with a 0.0098 percent average percentage error, and without being impacted by differences in fault distance, fault resistance, noise, or fault inception angle. Additionally, the optimised classifier reaches a 99.85% accuracy rate, enhancing line system dependability and advancing nuclear system development.

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“…In order to prevent further deterioration of the damage fault, hidden dangers need to be eliminated in time. With the continuous expansion of the scale of the line, its quality requirements are higher and higher [2]. Therefore, carry out patrol inspection on the line to ensure the reliability of power consumption.…”

Section: Introductionmentioning

confidence: 99%

Research on hidden danger identification of external damage of high voltage overhead transmission lines

Zheng

Shang

Chang

et al. 2023

Eighth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2022)

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A large number of aerial images are generated in the inspection of transmission lines. Due to the different sizes of conductors, insulators, shock absorbers and other components, the problem of rejection is easy to occur in the detection of external damage hazards, which affects the overall identification effect of lines. In this paper, a method to identify the hidden danger of external damage of high voltage overhead transmission lines is designed. From the three aspects of color, texture and shape, multi feature extraction is carried out on the line image to represent the location information of external damage hazards. The line image to be recognized is divided into uniform grids, and the coordinates and confidence of sub image blocks are used. Calculate the prediction accuracy of each region, filter the sub region with the largest value as the candidate target region, and output the corresponding prediction box. The deep transfer learning model is used to identify the hidden danger of external damage of the line, and the difference measurement index is used to evaluate the input feature vector. The difference feature results are fed back to the convolution layer to strengthen the interpretation ability of the difference information. The test results show that the design method improves the ability of image feature extraction, has high recognition accuracy, and provides decision-making basis for intelligent line early warning.

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Classification and localization of transmission line faults using curve fitting technique with Principal component analysis features

Cited by 12 publications

References 36 publications

Research on the accurate fault location of AC substations based on switching action logic

Research on the accurate fault location of AC substations based on switching action logic

Support Vector Machine Parameters Optimization for 500 kV Long OHTL Fault Diagnosis

Research on hidden danger identification of external damage of high voltage overhead transmission lines

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