# A Comprehensive Hybrid Deep Learning Approach for Accurate Status Predicting of Hydropower Units
## 2 Related Work  
Hydropower units operate under complex conditions, making their state prediction a key research area. Various methods, including physical models, statistical approaches, traditional machine learning, and deep learning techniques, have been explored to predict the operational status of hydropower systems. For example, [PERSON] et al. [28] employed a combination of Building Information Modeling (BIM) and computational fluid dynamics (CFD) to simulate turbine operations under different conditions and predict their hydraulic performance. [PERSON] et al. [29] utilized CFD and Fluid-Structure Interaction (FSI) models to analyze tidal turbine performance, showing that while rigid blade models can provide accurate predictions for small deformations, FSI models excel when dealing with larger deformations. However, physical model-based research methods often lack sufficient statistical validation. These models are typically optimized for specific systems or operating conditions, which limits their adaptability to different scenarios and broader operational conditions. To address these limitations and broaden the model's applicability, [PERSON] et al. [30] proposed a parameter-adaptive Support Vector Regression (SVR) model for predicting hydropower unit operational trends under varying conditions. Their model employed an enhanced Aquila optimizer and neural networks to automatically adjust the SVR's hyperparameters, enabling the real-time optimization of predictive performance in response to changing conditions. Compared with traditional SVR models, the proposed adaptive SVR significantly improves prediction accuracy across diverse operational scenarios and provides more precise forecasts of hydropower unit trends over specific timeframes. Similarly, [PERSON] et al. [31] developed an early fault detection method based on Gaussian Process Regression (GPR). By analyzing real-time monitoring data, their model assesses the health status of hydropower units and detects potential anomalies. The model incorporated nine key monitoring parameters and leverages Mahalanobis Distance (MD) to design state-monitoring indicators, allowing for the early detection of anomalies before they manifest in the unit's operation. To handle the uncertainties inherent in multi-dimensional monitoring data, [PERSON] et al. [32] proposed a comprehensive health status assessment method. This approach combines Gaussian Mixture Models (GMMs) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to perform multi-scale data cleaning and integrates the Gaussian Cloud Model (GCM) to construct health indicators (HIs) for the real-time health evaluation of hydropower units. This method effectively enhances the smoothness and stability of the evaluation curves. Nevertheless, while these methods have demonstrated excellent performance in capturing the health status and predicting failures under various operational conditions, they often lack the capability to address the complex dependencies between historical data and the current state. Many statistical and machine learning models, although adaptable to different operating conditions, tend to rely more on current measurement parameters when predicting future states, without fully considering the time series dependencies inherent in the state itself. Historical information plays a critical role in forecasting future states, and this limitation has led many researchers to explore deep learning methods such as recurrent neural networks (RNNs). RNNs possess powerful temporal dependency modeling capabilities, effectively capturing both long-term and short-term dependencies as states evolve over time. This makes them particularly well-suited for time series prediction tasks involving the historical operational data of hydropower units. [PERSON] et al. [33] combined Variational Mode Decomposition (VMD) with Hierarchical Temporal Memory (HTM) to decompose complex signals from hydropower units and applied HTM for anomaly detection, achieving higher detection accuracy and robustness.