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

Abstract

AbstractThe 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 the feeder data is not concerned yet. In this study, the authors present a dynamic data prepossessing method for parameter identification in PDN to successfully obtain a more accurate result. This method considers the similarities of feeder data in both spatial relationship and statistical theory, and then realizes a dynamic aggregation process for new coming data and obtains a set of data with tighter higher dimensional relationship for following identification task. In experiments, the authors applied this data processing method to the actual feeder data with no adjustment of the other condition; identification results with the authors’ processing achieve a 5.3% improvement in accuracy at most.