A Data Preprocessing Based on Cluster and Testing of Parameter Identification Method in Power Distribution Network

Abstract

We present a data prepossessing method for parameter identification based on clustering and hypothesis testing in a power distribution network to successfully achieve a more accurate result. This method considers the similarities of data in both spatial relationship and statistical theory, then builds a sophisticated data processing method to improve the performance of dynamic model-based parameter identification models, i.e., Markov chain Monte Carlo and sequential model-based global optimization. We applied this data processing method to the actual feeder data with no adjustment of the other condition. The experiment shows that our method achieves a 4.8% improvement in accuracy at most.

An Extensible Gradient-Based Optimization Method for Parameter Identification in Power Distribution Network

“…Other recent studies also have proposed the prediction results with the same metrics and the same dataset in this paper, such as the methods of MCMC and SMBO combined with clustering and hypothesis testing (denoted as MCMC C and SMBO C ). Li et al [32] published the best results of MAE values of MCMC C and SMBO C being 62.467 ± 0.366 and 61.868 ± 0.322, respectively. In another paper [31], the values of MAE computed by MCMC C and SMBO C are 62.136 ± 0.336 and 61.268 ± 0.311, respectively.…”

“…Te fundamental principles of PDN analysis can be found in references [20,[31][32][33]. To streamline the computational process, the assumption of balanced three-phase condition is made as a prerequisite for power fow calculations in this work.…”

“…Terefore, many advanced gradient descent algorithms can be utilized to accelerate the optimization. In addition, researchers pay more attention to data preprocessing methods, including the utilization of clustering algorithms and hypothesis testing [31,32].…”