Hydropower plants generate large volumes of data with high-dimensional time series, making early anomaly detection essential for monitoring, preventive maintenance and cost reduction. This study addresses the challenge of detecting anomalies in real time without labeled failure data by proposing an unsupervised approach that combines time series clustering, autoencoder-based models, and an adaptative anomaly thresholding. Initially, a clustering process is applied to historical time series data from multiple sensors in a hydroelectric power plant to identify groups of variables with similar temporal dynamics. Subsequently, for each cluster, various unsupervised models are trained to learn the normal behavior of the variables, including ARIMA, Autoencoders, Variational Autoencoders, Long Short-Term Memory networks, Sliding-window Autoencoders and Sliding-window Variational Autoencoders. Among these, the Autoencoder model demonstrated superior performance and was selected for real-time deployment. Finally, anomalies were detected by comparing predicted and actual values, using an adaptative threshold based on prediction errors. The system was tested on a real hydropower plant with over 150 time-dependent variables. The results show that 97% of the variables achieved an R² score above 0.8, with low MAE values indicating high reconstruction accuracy. The proposed approach, deployed in a real-time system integrated with Grafana dashboards, demonstrates the system’s capability to detect anomalies.