# A Comprehensive Hybrid Deep Learning Approach for Accurate Status Predicting of Hydropower Units
## 2 Related Work
This method not only effectively handles noise in complex signals but also provides accurate predictions of abnormal states in time series data. [PERSON] et al. [34] proposed a method based on Multi-Feature Selection (MFS) and Dilated Convolutional Graph Neural Networks (DCGNNs) to predict the swing trends in hydropower unit guide bearings. By utilizing multiple state variables to enhance prediction accuracy and convolutional networks to capture spatial dependencies within time series data, the proposed method improved both the precision and robustness of the predictions. [PERSON] et al. [24] introduced a degradation trend prediction model based on a Comprehensive Deterioration Index (CDI) and LSTM networks. This model combines time-domain and frequency-domain health models and utilizes key operational parameters such as active power, guide vane opening, and blade opening. By employing Ensemble Empirical Mode Decomposition (EEMD) alongside LSTM, the authors demonstrated that their method outperforms traditional machine learning techniques, including Support Vector Machines (SVMs) and Backpropagation Neural Networks (BPNNs), in terms of prediction accuracy. [PERSON] et al. [35] proposed a deep learning-based anomaly detection and classification system named MENSA, which integrates a Generative Adversarial Network (GAN) with an autoencoder. This method was validated in four smart grid scenarios, including hydropower stations, and exhibited high accuracy and robustness in addressing anomaly detection in time series power measurement data. [PERSON] et al. [36] presented a fault detection method for hydropower station generators utilizing different types of deep learning architectures, including LSTM and CNN. They simulated the operational status of hydropower units using synthetic data and compared the performance of various neural network models, highlighting the advantages of deep learning techniques such as LSTM in detecting anomalies in time series data. [PERSON] et al. [37] introduced an AI-based temperature-monitoring system focused on detecting and predicting temperature changes in hydropower units. By using LSTM to process time series data and combining it with Convolutional Autoencoders (CAEs) for anomaly detection, the authors demonstrated the advantages of deep learning models in predicting and monitoring the operational status of hydropower units. Although deep learning models have shown significant advantages in predicting and monitoring the operational status of hydropower units, such as the ability of LSTM networks to handle long-term dependencies in time series data, most existing studies have not fully accounted for the abrupt changes that can occur in hydropower station data. While standard LSTM networks can capture overarching trends in long sequences, their response to sudden changes in time series data tends to lag. Thus, enhancing the network architecture to better capture state transitions at critical moments is essential for improving prediction accuracy. To address this issue, this paper proposes the REST-LSTM, which introduces residual connections within the hidden state to enhance the model's responsiveness to short-term dependencies and abrupt changes in the data. Furthermore, while LSTM excels at capturing long-term dependencies, its ability to extract global temporal features remains limited. Therefore, this study also incorporates a TCN, which employs global convolutional operations to capture long-range temporal dependencies, thereby improving the model's capacity to detect complex time series patterns.