An Improved DC Line Fault Detection Scheme Using Zone Partition for MTDC Wind Power Integration Systems

Yang, Saizhao; Xiang, Wang; Wen, Jinyu

doi:10.1109/tpwrd.2021.3077473

Cited by 15 publications

(3 citation statements)

References 32 publications

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“…It can be applied to detect and recognize the faulted poles without long time delays. In [21], internal faults are partitioned into four zones along the transmission line. Then, the polarities and arrival times of traveling-waves are used for zone partition.…”

Section: Of 21mentioning

confidence: 99%

Locating Faults in Thyristor-Based LCC-HVDC Transmission Lines Using Single End Measurements and Boosting Ensemble

Swetapadma

Agarwal²,

Chakrabarti

et al. 2022

Electronics

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Most of the fault location methods in high voltage direct current (HVDC) transmission lines usemethods which require signals from both ends. It will be difficult to estimate fault location if the signal recorded is not correct due to communication problems.Hence a robust method is required which can locate fault with minimum error. In this work, faults are located using boosting ensembles in HVDC transmission lines based on single terminal direct current (DC) signals. The signals are processed to obtain input features that vary with the fault distance. These input features are obtained by taking maximum of half cycle current signals after fault and minimum of half cycle voltage signals after fault from the root mean square of DC signals. The input features are input to a boosting ensemble for estimating the location of fault. Boosting ensemble method attempts to correct the errors from the previous models and find outputs by combining all models. The boosting ensemble method has been also compared with the decision tree method and thebagging-based ensemble method. Fault locations are estimated using three methods and compared to obtain an optimal method. The boosting ensemble method has better performance than all the other methods in locating the faults. It also validated varying fault resistance, smoothing reactors, boundary faults, pole to ground faults and pole to pole faults. The advantage of the method is that no communication link is needed. Another advantage is that it allowsreach setting up to 99.9% and does not exhibitthe problem of over-fitting. Another advantage is that the percentage error in locating faults is within 1% and has a low realization cost. The proposed method can be implemented in HVDC transmission lines effectively as an alternative to overcome the drawbacks of traveling wave methods.

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Section: Of 21mentioning

confidence: 99%

Locating Faults in Thyristor-Based LCC-HVDC Transmission Lines Using Single End Measurements and Boosting Ensemble

Swetapadma

Agarwal²,

Chakrabarti

et al. 2022

Electronics

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“…The issue of global warming and climate change has drawn more public attention than ever before to the development of sustainable and renewable energy. Wind power has been acknowledged as a promising solution to the issue due to its indisputable advantages, such as sustainability, zero CO 2 emission and significant price drop recently [1,2]. Accordingly, wind turbines continue to grow in size and capability due to a surge in demand for wind power.…”

Section: Introductionmentioning

confidence: 99%

A high‐accuracy and robust diagnostic tool for gearbox faults in wind turbines

Lu,

Jiang,

et al. 2024

The Journal of Engineering

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Faulty gears are a common cause of wind turbine failures. For this sake, this work was developed as a reliable diagnostic tool for wind turbines to improve wind power stability accordingly. A convolutional extension neural network (CENN) was proposed to identify vibration and audio signals captured from a gearbox. According to the status of the contained faulty gears, a gearbox was categorised as one of the three types: (i) broken, (ii) rusty and (iii) a combination of (i) and (ii). It was further assigned one of the three severity levels: mild, moderate and severe. Therefore, there were a total of nine combinations for identification. Captured raw vibration and audio signals were applied to a chaotic synchronisation detector by which 3D chaotic error scatter feature images were generated to train and test the CENN. The recognition rate provided by CENN and the majority rule reached 99.6%, and then slightly fell to 97.4% in a noise robustness test, and consequently CENN outperformed counterparts in terms of the recognition rate and the robustness against noise. Accordingly, multiple gearbox faults can be well diagnosed for the first time in the literature. Finally, this paper concludes with a simplified version of the original proposal.

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“…The change rate of current measurements was used to identify the fault pole to discriminate the ground faults from the metallic return conductor (Haleem and Rajapakse, 2020). A DC line fault detection scheme was proposed using zone partition for multi-terminal DC wind power integration systems (Yang et al, 2021). A protection method based on the transient energy ratio was proposed where the fault identification criterion is put forward based on the impedance-frequency characteristic of the line boundary (Dai et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A novel primary and backup relaying scheme considering internal and external faults in HVDC transmission lines

Swetapadma

Agarwal²,

Abdelaziz

et al. 2022

Front. Energy Res.

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Discrimination of different DC faults near a converter end of a DC section consisting of a filter, a smoothing reactor, and a transmission line is not an easy task. The faults occurring in the AC section can be easily distinguished, but the internal and near-side external faults in the DC section are very similar, and the relay may cause false tripping. This work proposes a method to distinguish external and internal faults occurring in the DC section. The inputs are the voltage signals at the start of the transmission line and the end of the converter filter. The difference in voltage signals is calculated and given to an intelligent controller to detect and discriminate the faults. The intelligent controller is designed using machine learning (ML) and deep learning (DL) techniques for fault detection. The long short-term memory (LSTM-) based relay gives better results than other ML methods. The proposed method can distinguish internal from external faults with 100% accuracy. Another advantage is that a primary relay is suggested that detects faults quickly within a fraction of milliseconds. Nevertheless, another advantage is that a backup relay has been designed in case the primary relay cannot operate. Results show that the LSTM-based protection scheme provides higher sensitivity and reliability under different operation modes than the conventional traveling wave-based relay.

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An Improved DC Line Fault Detection Scheme Using Zone Partition for MTDC Wind Power Integration Systems

Cited by 15 publications

References 32 publications

Locating Faults in Thyristor-Based LCC-HVDC Transmission Lines Using Single End Measurements and Boosting Ensemble

Locating Faults in Thyristor-Based LCC-HVDC Transmission Lines Using Single End Measurements and Boosting Ensemble

A high‐accuracy and robust diagnostic tool for gearbox faults in wind turbines

A novel primary and backup relaying scheme considering internal and external faults in HVDC transmission lines

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