A dynamic data prepossessing method for parameter identification in power distribution network

The methods of building a model like Markov chain Monte-Carlo (MCMC) and sequential model-based global optimization (SMBO) in power distribution network (PDN) have achieved parameter identification successfully without extra measurement devices. However, the data processing focused on feeder is not concerned yet. In this study, authors present dynamic prepossessing method for PDN to obtain more accurate result. This considers similarities both spatial relationship statistical theory, then realizes aggregation process new coming obtains set with tighter higher dimensional following task. experiments, applied actual no adjustment other condition; results authors’ achieve 5.3% improvement accuracy at most.