Application of Grey Neural Network with Particle Swarm Optimization on Adaptive Setting Value Selection in Phase-selection Controller

Abstract

The power system is affected by different levels of transient shock while the AC filter is switching on. In order to minimize the transient impact, the circuit breaker is operated by the phase-selection controller using phase separation control which makes each phase breaker switching at the zero-crossing point of voltage. Therefore the accuracy of setting value selection in phase-selection controller will influence the switching impact. Statistics found that mechanical properties and electrical properties varied with mechanical wear and the aging of the components, which made the actual closing time change over time and deviate from the fixed value. Thus, a PSO-based grey neural network model is built to forecast the actual closing time. This model is optimized by PSO algorithm using the historical data. Finally, the predicted results show the effectiveness and feasibility of this method.