Multi-objective identification of critical distribution network assets in large interruption datasets. (May 2022) - Marcelino, C.G.; Torres, V.; Carvalho, L.; Matos, M.; Miranda, V.
Abstract: Performance indicators, such as the SAIFI and the SAIDI, are commonly used by regulatory agencies to evaluate the performance of distribution companies (DisCos). Based on such indicators, it is common practice to apply penalties or grant rewards if the indicators are greater to or less than a given threshold. This work proposes a new multi-objective optimization model for pinpointing the critical assets involved in outage events based on past performance indicators, such as the SAIDI and the System Average Interruption Duration Exceeding Threshold (SAIDET) indexes. Our approach allows to retrieve the minimal set of assets in large historical interruption datasets that most contribute to past performance indicators. A case study using a real interruption dataset between the years 2011–2104 from a Brazilian DisCo revealed that the optimal inspection plan according to the decision maker preferences consist of 332 equipment out of a total of 5873. This subset of equipment, which contribute 61.90% and 55.76% to the observed SAIFI and SAIDET indexes in that period, can assist managerial decisions for preventive maintenance actions by prioritizing technical inspections to assets deemed as critical. Highlights: A multi-objective model for detecting critical assets based on large outage datasets. Model goals: minimize number of assets and maximize contribution to SAIFI and SAIDET. A decision-making process to select the preferred solution from the Pareto set