Determination of Important Switches for Power Network Reconfiguration: A Comparative Study Between Permutation Importance and Statistical Analysis

In this study, a systematic method is presented for planning the network reconfiguration strategy to identify the most key switches or critical switches. This is to achieve a good voltage profile for distribution operation purposes. The optimal switching criterion is developed using permutation importance, a machine learning technique. Standard deviation is used to analyse the distribution of the load flow dataset. Permutation importance is a machine learning technique used to measure the contribution of an input to the output. Whereas standard deviation is a useful technique for measuring scattered data, it is used to identify values that are closer to the mean and aids in identifying the error margin of the outcomes. An overall load flow solution is carried out using the sample data of the average voltage profile of all the buses at different configurations under load-varying conditions. There may be a large number of feasible configurations based on switching sequences of the system, and out of that, some sample configuration data have been considered for testing purposes. This study presents a comparison between machine learning techniques and statistical models aimed at improving the performance of power networks to safeguard against irregular events.