Algorithms for automatic detection of faults/harmful events on 132-150 kV overhead lines

In the last years, statistics show a growing trend of harmful events caused by adverse weather. This trend suggests a revision of power systems management criteria, with the aim of reducing the effects of faults and/or harmful conditions. For this purpose, the availability of additional information about the status of the power system can allow a significant improvement compared with the current standards. In particular, additional information concerning the mechanical state of the lines can allow automatic detection of some types of harmful events. This work proposes some algorithms for the automatic detection of some types of events on overhead lines (OHLs) of the 132 kV and 150 kV sub-transmission networks, as these voltage levels show the highest number of faults. As to the causes of faults, Italian data show that the long supply interruptions are mainly caused by contact with (fall of) trees, atmospheric events (lightening, wind, snow, ice), and conductors and components wear. The algorithms described in this work are based on the mechanical monitoring of OHLs and aim for the automatic detection of the following three types of events: conductor breaking, fall of trees on the line conductors, snow/ice accretion on the line conductors. In addition, the proposed approach would allow an approximate location of these events along the involved OHLs. Overall, automatic detection of the type of event and its location along a line can provide significant advantages concerning the operation and management of the power system. On the other hand, the mechanical monitoring of OHLs requires the installation of a limited number of additional sensors and a limited overall cost in comparison with the expected benefits.