Fault diagnosis method for hydropower unit via the incorporation of chaotic quadratic interpolation optimized deep learning model

Hydro-turbine fault diagnosis is crucial for hydropower plants’ safe and stable operation. This paper proposes a deep learning model for hydro-turbine fault diagnosis based on chaotic quadratic interpolation optimization (CQIO). Chaotic mapping optimizes the initial population of the QIO algorithm by introducing randomness and diversity, improving the algorithm’s performance and stability. The CQIO is used to calculate the optimal hyperparameter combinations for CNN-LSTM models. It can improve the model’s stability and reduce computational resource consumption. The results show that the fault accuracy of the proposed method reaches 96.7% and 93.6%, respectively, which is higher than the CNN, LSTM, CNN-LSTM, and QIO-CNN-LSTM models. Notably, the diagnostic accuracy about impact faults exceeds that of wear faults, with the latter exhibiting an augmented diagnostic accuracy as sediment increases.