Local current-based method for fault identification and location on series capacitor-compensated transmission line with different configurations

Elmitwally, A.; Ghanem, Abdelhady

doi:10.1016/j.ijepes.2021.107283

Cited by 11 publications

(3 citation statements)

References 27 publications

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“…In contrast to research initiatives akin to this study, which predicate their analyses on electrical networks possessing a specific set of fundamental components while omitting critical elements integral to the network's fault response dynamics, which have been noted in [1,43,44], and introducing a solution, it is evident that such theories, although potentially proficient [21], remain confined in their applicability to a select category of networks. These theories are inherently limited in their generalizability to more complex and scientifically representative network configurations that exist in practical reality.…”

Section: Network Under Considerationmentioning

confidence: 96%

Transmission Line Fault Classification Based on the Combination of Scaled Wavelet Scalograms and CNNs Using a One-Side Sensor for Data Collection

Altaie,

Abderrahim,

Alkhazraji

2024

Sensors

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This research focuses on leveraging wavelet transform for fault classification within electrical power transmission networks. This study meticulously examines the influence of various parameters, such as fault resistance, fault inception angle, fault location, and other essential components, on the accuracy of fault classification. We endeavor to explore the interplay between classification accuracy and the input data while assessing the efficacy of combining wavelet analysis with deep learning methodologies. The data, sourced from network recorders, including phase currents and voltages, undergo a scaled continuous wavelet transform (S-CWT) to generate scalogram images. These images are subsequently utilized as inputs for pretrained deep learning models. The experiments encompass various fault scenarios, spanning distinct fault types, locations, times, and resistance values. A remarkable feature of the proposed work is the attainment of 100% classification accuracy, obviating the need for additional algorithmic enhancements. The foundation of this achievement is the deliberate selection of the right input. The decision to employ an identical number of samples as the number of scales for the CWT emerges as a pivotal factor. This approach underpins the high accuracy and renders supplementary algorithms superfluous. Furthermore, this research underscores the versatility of this approach, showcasing its effectiveness across diverse networks and scenarios. Wavelet transform, after rigorous experimentation, emerges as a reliable tool for capturing transient fault characteristics with an optimal balance between time and frequency resolutions.

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Section: Network Under Considerationmentioning

confidence: 96%

Transmission Line Fault Classification Based on the Combination of Scaled Wavelet Scalograms and CNNs Using a One-Side Sensor for Data Collection

Altaie,

Abderrahim,

Alkhazraji

2024

Sensors

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“…Furthermore, the robustness of the artificial neural network protection scheme proposed by Gilany et al (2010) has several practical constraints to be fabricated, as the reliability of these systems is highly determined by the nature of the design and the level of changes made to it, such as higher increased loads. In Elmitwally and Ghanem (2021), detecting the fault, selecting the faulty phase, and identifying the fault point depended on measuring the three-phase current at the protective relay in superimposed components of compensated TLs. In Khoshbouy et al (2022), the ratio for the phase voltage at the sending end to the sum of the ratio for the phase current for two ends in TLs, called pilot superimposed impedance, was used to detect interior and exterior faults competed to a predetermined value, which was estimated based on Thevenin impedance.…”

Section: Literature Overviewmentioning

confidence: 99%

Simultaneous series and shunt earth fault detection and classification using the Clarke transform for power transmission systems under different fault scenarios

Esmail,

Alsaif,

Abdel Aleem

et al. 2023

Front. Energy Res.

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For high-voltage (symmetric and non-symmetric) transmission networks, detecting simultaneous faults utilizing a single-end-based scheme is complex. In this regard, this paper suggests novel schemes for detecting simultaneous faults. The proposed schemes comprise two different stages: fault detection and identification and fault classification. The first proposed scheme needs communication links among both ends (sending and receiving) to detect and identify the fault. This communication link between both ends is used to send and receive three-phase current magnitudes for sending and receiving ends in the proposed fault detection (PFD) unit at both ends. The second proposed scheme starts with proposed fault classification (PFC) units at both ends. The proposed classification technique applies the Clarke transform on local current signals to classify the open conductor and simultaneous faults. The sign of all current Clarke components is the primary key for distinguishing between all types of simultaneous low-impedance and high-impedance faults. The fault detection time of the proposed schemes reaches 20 ms. The alternative transient program (ATP) package simulates a 500 kV–150-mile transmission line. The simulation studies are carried out to assess the suggested fault detection and identification and fault classification scheme performance under various OCFs and simultaneous earth faults in un-transposed and transposed TLs. The behavior of the proposed schemes is tested and validated by considering different fault scenarios with varying locations of fault, inception angles, fault resistance, and noise. A comparative study of the proposed schemes and other techniques is presented. Furthermore, the proposed schemes are extended to another transmission line, such as the 400 kV–144 km line. The obtained results demonstrated the effectiveness and reliability of the proposed scheme in correctly detecting simultaneous faults, low-impedance faults, and high-impedance faults.

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“…Chen et al [11] analyzed the causes of high-resistance faults producing high-frequency components by using zero a sequence equivalent network, and proposed At present, there are many studies on the identification of single-phase ground fault types. According to the different methods of the application of characteristics, the existing identification methods can be divided into two categories: one is the characteristic analysis method [2][3][4][5][6][7][8][9][10][11][12][13][14][15] such as: Zhou et al [5] proposes an online monitoring and identification method for high-resistance grounding faults in distribution networks, using high-order harmonics generated during high-resistance grounding faults as the judgement basis. Shen et al [6] proposed a method for identifying single-phase arc grounding faults in distribution networks which proved that compared with metal grounding faults, arc grounding faults have a continuous transient process.…”

Section: Introductionmentioning

confidence: 99%

Single-Phase Grounding Fault Types Identification Based on Multi-Feature Transformation and Fusion

Min

Xia

Meng

et al. 2022

Sensors

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The frequent occurrence of single-phase grounding faults affects the reliable operation of power systems. When a single-phase grounding fault occurs, it is difficult to accurately identify the fault type because of the weak characterization and subtle distinction between different fault types. Therefore, this paper proposes a single-phase grounding fault type identification method based on the multi-feature transformation and fusion. Firstly, the Hilbert–Huang transform (HHT) was used to preprocess the fault recorded wave data to highlight the characteristics between different fault types. Secondly, the deep learning model ResNet18 and the long short-term memory (LSTM) are designed to extract the complex abstract features and time-series correlation features from the preprocessed data set separately. Finally, it designs a fusion model to combine the advantages of heterogeneous models to identify the type of single-phase grounding fault. Experiments validate that the method is good at fully mining the characteristics of the fault types contained in the fault recorded wave data, so it can identify multiple types of faults with strong robustness and provide a reliable basis for the subsequent formulation of targeted fault-handling measures.

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Local current-based method for fault identification and location on series capacitor-compensated transmission line with different configurations

Cited by 11 publications

References 27 publications

Transmission Line Fault Classification Based on the Combination of Scaled Wavelet Scalograms and CNNs Using a One-Side Sensor for Data Collection

Transmission Line Fault Classification Based on the Combination of Scaled Wavelet Scalograms and CNNs Using a One-Side Sensor for Data Collection

Simultaneous series and shunt earth fault detection and classification using the Clarke transform for power transmission systems under different fault scenarios

Single-Phase Grounding Fault Types Identification Based on Multi-Feature Transformation and Fusion

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