Prioritization of Medium-Voltage Wiring Actions With Artificial Intelligence Algorithm
In power distribution networks, keeping quality of service indexes at high levels (SAIDI and SAIFI) can be accomplished through preventive maintenance interventions, which are scheduled by utility’s maintenance planning professionals. They are responsible for planning maintenance actions consisting of replacing damaged and obsolete devices and installing new devices, while respecting a certain budget availability. Among all possible actions, spacer cable (SC), phase separators (FS) and tree pruning (TP) are aimed to correct issues on medium-voltage wiring (MVW). Through these actions, impending wiring-related failures are mitigated or even avoided. Currently, utilities’ planning professionals may inaccurately determine the annual set of maintenance actions, due to the use of recorded measurements, data from multiple convoluted spreadsheets and personal experience. This paper presents the development of an automated computational tool aimed to prioritize MVW-related maintenance actions, assisting planning professionals in optimizing the use of annual available budget.