Heterogeneous Multi-ml Approach Implemented in HVDC Transmission Line

EasyChair Preprint 9597

Abstract

The research article describes a system of machine architecture for protection of transmission lines of HVDC, in which multiple models of ML (KNN and SVM) are employed for fault classification and recognition. The K-Nearest Neighbor classifier is intended to serve two functions. It detects the type of fault as well which serves as a backup module for the starting unit's doubtful fault declaration. From a single-end single measurement, a feature vector consisting of standard deviations gradients and of DC and harmonic current, DC voltage, and correlation coefficients is retrieved. By simulating different states that are non-fault and fault states on a data set having training and test cases are obtained. The ML algorithm is trained in MATLAB and evaluated on a total of 2220 severe instances. The acquired results demonstrated the efficacy of the suggested method in detecting and distinguishing between various internal and external/pseudo problems.In this paper we will discuss how the ANN model Simulink in the MATLAB is used for researching, collecting, and evaluating 456 data sets.

Keyphrases: DC voltage ML models, HVDC transmission, fault classification.

@booklet{EasyChair:9597,
  author    = {S.Faiza Nasim and Bareerah Rahman and Umme Kulsoom},
  title     = {Heterogeneous Multi-ml Approach Implemented in HVDC Transmission Line},
  howpublished = {EasyChair Preprint 9597},
  year      = {EasyChair, 2023}}