Detecting Faults in VSC-HVDC Systems by Deep Learning and K-means

Title:Detecting Faults in VSC-HVDC Systems by Deep Learning and K-means

Volume: 14 Issue: 4

Author(s): Roohollah Sadeghi Goughari, Mehdi Jafari Shahbazzadeh*Mahdiyeh Eslami

Affiliation:

• Department of Electrical Engineering, Kerman Branch, Islamic Azad University, Kerman,Iran

Keywords: Fault location, VSC-HVDC lines, deep learning, wavelet analysis, PMU, K-means algorithm.

Abstract: Background: The present paper compared two methods that are employed to determine the fault location in VSC-HVDC transmission lines. These systems are widely recognized for their fast and reliable control.

Methods: Wavelet transform was employed as an advanced technique of signal processing to extract important characteristics of fault signal from both sides of the line by phasor measurement unit (PMU). Deep Learning was implemented to identify the relationship between the extracted features from the wavelet analysis of fault current and variations under fault conditions. Wavelet transform and advanced signal processing techniques were adopted to extract important features of fault signals from both sides of the line by PMU.

Results: The results indicated the high accuracy of finding fault location by the deep learning algorithm method compared to the k-means algorithm with an error rate of <1%.

Conclusion: Studies on the 50 kV VSC-HVDC transmission line with a length of 25 km in MATLAB have been conducted.

Cite this article as:

Goughari Sadeghi Roohollah , Shahbazzadeh Jafari Mehdi *, Eslami Mahdiyeh , Detecting Faults in VSC-HVDC Systems by Deep Learning and K-means, Recent Advances in Electrical & Electronic Engineering 2021; 14 (4) . https://dx.doi.org/10.2174/2352096513999201105155206

DOI https://dx.doi.org/10.2174/2352096513999201105155206	Print ISSN 2352-0965
Publisher Name Bentham Science Publisher	Online ISSN 2352-0973