WRC-SDT Based On-Line Detection Method for Offshore Wind Farm Transmission Line

Wang, X. D.; Gao, Xin; Liu, Y. M.; Wang, Y. W.

doi:10.1109/access.2020.2981294

Cited by 19 publications

(15 citation statements)

References 42 publications

(50 reference statements)

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“…The C matrix is indirect fault matrix, which is used to generate the fault characteristic matrix. The C matrix can be calculated by [27]:

C_{168 \times 335} = {(||, boldS T_{168 \times 335})}^{2}

…”

Section: Fault Eigenvalues Selection Based On Stockwell‐transform and Pearson Correlation Coefficientmentioning

confidence: 99%

“…CART algorithm is an improved algorithm based on ID3 and C4.5. CART has excellent anti‐noise performance and is easy to understand and use, which is very suitable for real‐time fault classification [27]. The Gini coefficient is the basis for CART algorithm to select fault eigenvalues.…”

Section: Double‐terminal Fault Location Based On Random‐forestmentioning

confidence: 99%

“…The ST is a combination of STFT and wavelet transform, which retains the advantages of STFT and wavelet transform [33]. The phase information is effectively maintained and the robustness to MN is improved by ST [27]. In the proposed method, the ST can be defined by

S (α, f) = S \{g (t)\} = \int_{- \infty}^{\infty} g ((), t) \frac{|f|}{\sqrt{2 π}} {normale}^{- \frac{{((), α - t)}^{2} f^{2}}{2}} {normale}^{- i 2 π f t} d t

where f is frequency; α and t are time variables; g ( t ) is input signal.…”

Section: Fault Eigenvalues Selection Based On Stockwell‐transform And...mentioning

confidence: 99%

“…When the system fails, the crowbar and internal control strategy of the wind turbine can restrain the change of voltage and current. Therefore, the existing fault methods cannot be directly applied to the DOWF fault research [27,28].…”

Section: Introductionmentioning

confidence: 99%

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Stockwell‐transform and random‐forest based double‐terminal fault diagnosis method for offshore wind farm transmission line

Wang

Gao

Liu

et al. 2021

IET Renewable Power Gen

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Due to the difficulty and time-consumption in locating short-distance transmission lines for deep-sea offshore wind farm (DOWF),this paper proposes a novel double-terminal fault location method by using Stockwell-transform (ST) and random-forest (RF). After the fault type and branch are accurately determined, the accurate transmission line fault location is located. Firstly, Stockwell-transform is employed to extract fault eigenvalues from the collected wind turbine (WT) current signals, which will reduce the sensitivity of eigenvalues to noise. And the Pearson correlation coefficient (PCC) is introduced to remove duplicate eigenvalues. Secondly, the reserved fault eigenvalues are taken as inputs to the different random-forest to classify fault types and identify the fault branch, respectively. Finally, the double-terminal fault location principle is established in fault negative sequence network (only ABCG uses positive sequence components). Newton-Raphson method (NRM) is used to eliminate the influence of asynchrony data, which implies an accurate transmission line fault location for deep-sea offshore wind farm. More than 4000 fault cases data obtained by Simulink simulation verify the feasibility and performance of the proposed method. The results show that the proposed location method has a high fault recognition rate and is immune to fault inception angle, resistance, location and noise.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…The C matrix is indirect fault matrix, which is used to generate the fault characteristic matrix. The C matrix can be calculated by [27]:

C_{168 \times 335} = {(||, boldS T_{168 \times 335})}^{2}

…”

Section: Fault Eigenvalues Selection Based On Stockwell‐transform and Pearson Correlation Coefficientmentioning

confidence: 99%

Section: Double‐terminal Fault Location Based On Random‐forestmentioning

confidence: 99%

S (α, f) = S \{g (t)\} = \int_{- \infty}^{\infty} g ((), t) \frac{|f|}{\sqrt{2 π}} {normale}^{- \frac{{((), α - t)}^{2} f^{2}}{2}} {normale}^{- i 2 π f t} d t

where f is frequency; α and t are time variables; g ( t ) is input signal.…”

Section: Fault Eigenvalues Selection Based On Stockwell‐transform And...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Stockwell‐transform and random‐forest based double‐terminal fault diagnosis method for offshore wind farm transmission line

Wang

Gao

Liu

et al. 2021

IET Renewable Power Gen

Self Cite

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“…Many industries can not afford to continuously depend on a system of poor efficiency and reliability. As transmission lines may be positioned in obscure areas, proper techniques must be used to ensure that the stability of the transmission lines can be detected to avoid continuity in power supplies [23]. In a MATLAB-Based GUI Development for the Detection and Localization of Faults in Transmission Line conducted by Lala, Karmakar, and Singh, MATLAB-based simulation with the utilization of graphical user interface or GUI has been proven to obtain a better level of accuracy and precision in terms of obtaining results [24].…”

Section: Review Of Related Literaturementioning

confidence: 99%

A Simulink Model for Transmission Line Fault Detection

2020

IJETER

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As cities throughout the world continue to develop, more and more transmission lines are utilized to be able to deliver electrical power for varying uses. Transmission lines hold a great responsibility in delivering power. Like all things, faults and interference can be created by foreign objects, obstructions, and natural calamities. Due to the importance of transmission lines, it is important to be able to detect faults in the lines to be able to minimize losses in power and revenue. Through this study, the software MATLAB will be utilized to successfully locate faults in transmission lines through various simulations. This study will attempt a simulation of a fault line detection method in Matlab. The researchers will utilize Matlab's toolboxes and Simulink to emulate a transmission line with a fault that will then be detected. The idea is to create a simulation model as a basis for future studies. Future studies will then be able to test other methods by building on the model created in this study. If their method is proven to be doable and effective, it could prove to be not only a more convenient method compared to direct circuit analysis but it could also be a more cost-efficient alternative for transmission line fault location.

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