This invention discloses a method for monitoring the state trends of
hydropower units based on two-stage
signal decomposition and the IBiLSTM model. The method involves collecting vibration signals from the
hydropower units for preprocessing; constructing the ITGCOA optimization
algorithm and designing a
fitness function; using the ITGCOA optimization
algorithm to adaptively optimize the SVMD and BAACMD models, achieving initial
decomposition of the preprocessed
signal and secondary
decomposition of the sub-mode component with the highest
center frequency obtained from the initial decomposition; calculating the
fuzzy entropy values of the remaining sub-mode components for reconstruction; and fusing the high-frequency feature sub-sequences obtained from the secondary decomposition with the reconstructed feature sub-sequences to construct the input sequence of the prediction model. This input sequence is then used to construct the IBiLSTM prediction model for monitoring the state trends of
hydropower units, achieving high-precision prediction. Compared with existing technologies, this invention improves the efficiency and accuracy of hydropower unit state
trend prediction, accurately warns of abnormal unit operating conditions, ensures the safe and stable operation of the units, and improves the
overall efficiency of the power
plant.