A Breaker Failure Detection Method Based on Nonlinear Parameter Estimation for Modular Hybrid DC Circuit Breakers

Abstract

Modular hybrid dc circuit breakers (DCCBs) have been widely employed to protect multiterminal high-voltage dc (MT-HVdc) grids. However, breaker failures still pose a challenge to the protection of MT-HVdc grids, especially for a single module failure of breakers. To this end, a breaker failure detection method based on nonlinear parameter estimation is presented. This proposed method consists of three parts. The first is a signal preprocessing method that eliminates the effect of the RC circuit of the hybrid DCCB. The second is an offline optimal parameter acquisition method that has an improved numerical model of the metal–oxide varistor (MOV) and combines the Levenberg–Marquardt (LM) algorithm with the particle swarm optimization (PSO) algorithm. The third is an online breaker voltage error calculation that estimates the breaker voltage based on the improved numerical MOV model to detect the partial failure of the modular hybrid DCCB. The performance of the proposed algorithm is evaluated by both power system computer aided design (PSCAD)/electro-magnetic transient design and control (EMTDC) tests and field tests. The study results prove the effectiveness of the proposed method for the partial failure of the modular hybrid DCCB considering different faulty conditions and the aging of MOVs.