Supervised relevance vector machine based dynamic disturbance classifier for series compensated transmission line

Abstract

Background: Nowadays, numerical relays configured through power swing blocking function are vulnerable to faults with high impedance owing to nonlinearities present in the faulty path. Identification of symmetrical high impedance faults incepting during the power swing scenario is of prime importance for protection engineers to avoid cascade tripping. It is analyzed that the classical approaches for symmetrical fault detection during power swing can maloperate owing to high level of uncertainty arise during validation of high impedance fault, symmetrical faults and cross country faults during power swing with varied level of compensation which has been explored in this research.Aims: This paper presents an innovative scheme for series compensated line by utilizing the capability of Relevance Vector Machine (RVM) as a disturbance classifier taking into consideration of cross country faults, CT saturation; power swings prone to symmetrical faults along with high impedance faults in comparison with prevailing methods. Materials & Methods:In order to validate the proposed RVM based disturbance classifier algorithm, different kinds of faults and power swing situations have been applied in IEEE 9 bus test system using PSCAD/EMTDC software followed by validation in MATLAB. The features of sequence components of currents are estimated using Modified Full Cycle Discrete Fourier Transform (MFCDFT). The extracted features are used as control inputs to RVM for discernment among power swing and fault under the influence of wide range of system and fault dynamics.

Accurate fault detection during power swing: compensated line study

“…Employing the fuzzy logic technique over the phase angle of positive-sequence current is the method of fault detection in an SCC line described in (Mishra and Yadav, 2018). In (Patel et al , 2020b), an innovative scheme for series compensated line fault detection based on the relevance vector machine as a disturbance classifier is suggested.…”