A data‐driven measurement placement to evaluate the well‐being of distribution systems operation

The widespread integration of intelligent electronic devices has facilitated the employment of data mining methods in evaluating the operating condition of distribution systems. This possibility comes to prominence in active networks, where distributed energy resources can cause unforeseen dynamics that requires an effective monitoring infrastructure and a fast‐track procedure to convey the system operating condition in a comprehensible manner to the operator. To this end, a data‐driven approach is proposed to assess the status of system operating constraints by presenting each constraint as a classification problem. Afterwards, by exploiting the propounded presentation of the system operating condition, the measurement placement problem in distribution systems is addressed as selecting a set of features that have the most contribution to evaluating the system operating status . To do so, first, the effectiveness of the measurement units is identified through their contribution to the classification process, and then a procedure is proposed to pinpoint the measurement units with redundant information. Monte–Carlo simulations are performed to provide a comprehensive training set. Receiver operating characteristic analysis and time‐series power flows demonstrate the effectiveness of the proposed approaches.