A Support Vector Machine Learning-Based Protection Technique for MT-HVDC Systems

• Published in: Energies (Basel) Vol. 13; no. 24; p. 6668
• Main Authors: Muzzammel, Raheel, Raza, Ali
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2020
• Subjects: Algorithms; Alternative energy sources; Classification; Datasets; DC grid protection; Decision making; Energy resources; fault classification; Fault diagnosis; fault identification; fault location; Feature selection; Fourier transforms; Lagrange multiplier; Machine learning; MT-HVDC transmission systems; Principal components analysis; Renewable resources; Signal processing; support vector machine (SVM); Support vector machines; Test systems; Wavelet transforms
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en13246668

High voltage direct current (HVDC) transmission systems are suitable for power transfer to meet the increasing demands of bulk energy and encourage interconnected power systems to incorporate renewable energy sources without any fear of loss of synchronism, reliability, and efficiency. The main challenge associated with DC grid protection is the timely diagnosis of DC faults because of its rapid built up, resulting in failures of power electronic circuitries. Therefore, the demolition of HVDC systems is evaded by identification, classification, and location of DC faults within milliseconds (ms). In this research, the support vector machine (SVM)-based protection algorithm is developed so that DC faults could be identified, classified, and located in multi-terminal high voltage direct current (MT-HVDC) systems. A four-terminal HVDC system is developed in Matlab/Simulink for the analysis of DC voltages and currents. Pole to ground and pole to pole faults are applied at different locations and times. Principal component analysis (PCA) is used to extract reduced dimensional features. These features are employed for the training and testing of SVM. It is found from simulations that DC faults are identified, classified, and located within 0.15 ms, ensuring speedy DC grid protection. The realization and practicality of the proposed machine learning algorithm are demonstrated by analyzing more straightforward computations of standard deviation and normalization.