Fault Detection of Cooling System Based on Long Short-Term Memory at Nam Ngum 1 Hydropower Plant

Synchronous generators play a vital role in hydropower plants, making reliable cooling systems essential to prevent severe performance issues and potential failures. Traditional maintenance methods for these cooling systems often rely on periodic inspections or time-based strategies, which may be limited in their ability to detect subtle or evolving faults. This paper introduces a deep-learning approach using Long Short-Term Memory (LSTM) networks for fault detection in the cooling system of the Nam Ngum-1 (NNG-1) hydropower plant in Lao PDR. The methodology involves preprocessing historical operational data to extract key features, which are then used to train the LSTM network. This network is designed to learn complex temporal patterns associated with various fault conditions. The model’s effectiveness is assessed through accuracy, precision, and recall metrics. The proposed approach not only enhances fault detection but also minimizes downtime, and optimizes maintenance schedules, ultimately improving the overall reliability and efficiency of the hydropower plant.