Resumo:
Hydropower plants installed in the Amazon basin are negatively influenced by transporting
logs and sediments from the river bed. All transported material accumulates in the
protection grids, which are installed in the water intake of the generating units to prevent
the entry of this material and avoid damage to the turbines, which would result in
economic losses. The accumulation of material on the protection grids reduces the water
drop and, consequently, the flow available for generation, which results in load losses, preventing
the generation unit from operating at full capacity. The presented problem occurs
at the Jirau hydropower plant, located on the Madeira River and has 50 generation units,
each with the potential to generate 75MW at total capacity. This plant operates on a
run-of-river basis, meaning there is no reservoir for water storage. Therefore, all water resources
must be used when available. The fact that it operates on a run-of-river, together
with the problem of transporting logs and sediments, brings challenges to the operation
of the plant, as the sediments that accumulate in the protection grids and consequently
reduce the generation potential require the complete shutdown of the generating units so
that the accumulated dirt can decant, thus enabling the resumption of the waterfall, and
consequently the flow available for generation. A large number of generation units and the
different location dispositions of equipment in the river alter the sediment accumulation
profile, making it difficult to define rules that define the necessary stoppage time for each
unit to decant the dirt and restart generation at total capacity. In this scenario, instead of
relying only on the experience of operators, developing efficient methods capable of determining
the ideal stoppage time for each generation unit becomes essential. In this work,
prediction models using Bayesian Networks and Hidden Markov Models are proposed to
estimate the downtime required for decanting the dirt from each generation unit so that
it can be used again for generation in the shortest possible time. Big Data and Analytics
techniques are also used to collect and process the large volume of data existing at the hydroelectric
plant. The results demonstrate that the developed models could satisfactorily
infer the time required for sediment decantation. The resulting model makes it possible to
query information using various information, including the obstruction level when a unit
stops, the obstruction level at restart time, whether neighboring units are operating, and
in which power range.
