MONITORING AND EARLY WARNING ABNORMALITIES OF THE HYDROELECTRIC RESERVOIR DAMS USING WSN AND AI

Abstract

In this study, we use a wireless sensor network (WSN) to acquire data from the expansion gaps
between the concrete blocks of the hydroelectric reservoir dams. The data was analyzed, processed and
stored for other operations of system. The combination of using artificial intelligence (AI) to provide
information for early warning abnormalities of hydroelectric reservoir dams. A monitoring interface was
designed on the computer to display amplitude parameters, status of expansion gaps and provide early
warnings about the amplitude of expansion joints, which helps operators conveniently observe the status
of the reservoir dams in the present and near future. From there, it is possible to plan or propose the
solutions to prevent the risks, that contribute to the safe and effective operation of the hydroelectric
reservoir dams system.