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Estimation of the Arc Model Parameters Using Heuristic Optimization Methods

The black box arc model is a valuable tool to describe the switching during the arcing time in AC and DC circuit breakers. It can provide an efficient approach to integrate the arc model in the network for investigating the interaction between arc and network. However, the reliable determination of arc model parameters based on the voltage and current waveforms is a challenging issue. This paper presents an estimation approach to the arc parameters based on the linear and nonlinear description of the Mayr arc model concerning the sinusoidal and non-sinusoidal current waveforms. Accordingly, Heuristic optimization methods such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have been used to assess the algorithms and for evaluating the parameters of arc models.