Abstract
In the fault classification and identification of flexible DC transmission lines, it is inevitable to use the voltage and current characteristics of the transmission line. All kinds of data transformation methods can highlight the hidden characteristics of the original fault electrical quantity. Various artificial intelligence algorithms can further reduce the difficulty of transmission line fault classification. For such fault classification methods, this paper first builds a four-terminal flexible direct current transmission system model on PSCAD/EMTDC platform and obtains data by simulating different faults of transmission lines. Then, empirical mode decomposition (EMD), wavelet transform (WT), fast Fourier transform (FFT), and variational mode decomposition (VMD) are performed on the obtained data, respectively. Finally, the transformed data and original data are used as inputs to classify by convolutional neural network (CNN). The influence of one data transformation method and different combinations of two data transformation methods on CNN classification results is explored. The simulation results show that when only one data transformation method is used, CNN has the best classification effect for the data after VMD transformation. The classification accuracy and recall rate are both increased from 96.9% and 96.3% without data transformation to 99.88%. When VMD and FFT are combined, CNN classification results’ accuracy and recall rate are further improved to 99.96%.
Highlights
Modular multilevel converter based high voltage direct current (MMC-HVDC) has many advantages, such as modular structure, low harmonic content, the independent control system of active and reactive power, and low switching loss at runtime. erefore, MMC-HVDC has shown broad application prospects in isolated island power supply, offshore wind power integration, power supply in large cities, weak AC grid connection, long-distance largecapacity transmission, and asynchronous interconnection [1,2,3,4].e DC transmission network is a low inertia system [5]
In order to explore the influence of different data transformation methods on convolutional neural network (CNN) classification of flexible DC transmission lines and meet the rapid requirements of DC transmission line fault classification and identification, this paper first obtains the fault data of the DC transmission line within 2.5 ms after a fault and transforms the original data by empirical mode decomposition (EMD) [21], wavelet transform (WT) [22], fast Fourier transform (FFT) [23], and variational mode decomposition (VMD) [24]. en, the transformed data and the original data are used as input to classify the CNN. e effects of one data transformation method and different combinations of two data transformation methods on CNN classification results are explored
The CNN classification results are the best when three-layer VMD is applied to the fault current i21, and i31 and the third layer modal component IMF3 are taken. ree-layer VMD is applied to the fault current between 2 s and 2.01 s in Figures 2 and 3, and the three-layer modal components obtained are shown in Figures 11 and 12
Summary
Modular multilevel converter based high voltage direct current (MMC-HVDC) has many advantages, such as modular structure, low harmonic content, the independent control system of active and reactive power, and low switching loss at runtime. erefore, MMC-HVDC has shown broad application prospects in isolated island power supply, offshore wind power integration, power supply in large cities, weak AC grid connection, long-distance largecapacity transmission, and asynchronous interconnection [1,2,3,4]. E data-driven artificial intelligence classification algorithm is not affected by the transmission speed of fault traveling waves It can only rely on fault data to realize the classification and identification of transmission line faults. In [18], the fast Fourier transform and discrete wavelet transform were used to extract the feature of voltage and current after fault, and the adaptive neurofuzzy inference system was applied to realize the classification and identification of fault types. This method was not suitable for situations of unbalanced load and asynchronous sampling. In order to explore the influence of different data transformation methods on CNN classification of flexible DC transmission lines and meet the rapid requirements of DC transmission line fault classification and identification, this paper first obtains the fault data of the DC transmission line within 2.5 ms after a fault and transforms the original data by EMD [21], WT [22], FFT [23], and VMD [24]. en, the transformed data and the original data are used as input to classify the CNN. e effects of one data transformation method and different combinations of two data transformation methods on CNN classification results are explored
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
