Hydropower is the cornerstone of a clean energy system. China's hydropower installed capacity has long ranked first in the world. With the promotion of the "dual carbon" goal, the role of hydropower stations in the power system has shifted from simple power generation units to undertaking multiple tasks such as peak shaving, frequency regulation, and backup. At the same time, the equipment maintenance and reservoir scheduling of hydropower stations are facing unprecedented complexity. The landing of artificial intelligence technology in this field is rewriting the operation mode of traditional hydropower stations.
##Predictive maintenance of equipment: from 'repair when broken' to 'early warning'
The core equipment of hydropower stations includes turbines, generators, main transformers, speed control systems, gate opening and closing machines, etc. The traditional maintenance mode mainly relies on regular maintenance, which means stopping the machine for inspection at a fixed cycle. This approach may result in excessive maintenance waste or sudden failures during maintenance intervals. The idea of AI predictive maintenance is to use sensor data to continuously evaluate the health status of equipment and provide warnings before faults occur.
Vibration monitoring is one of the most mature application directions in hydroelectric units. The vibration signals generated by the unit during operation contain rich state information, such as rotor cavitation, bearing wear, rotor imbalance, and other anomalies that leave characteristics on the vibration spectrum. State recognition models based on deep learning, such as Convolutional Neural Networks (CNN) and Long Short Term Memory Networks (LSTM), can learn from vibration time-series data and identify differences between normal and abnormal operating conditions. Some hydropower stations have deployed unit condition monitoring systems based on acoustic feature analysis, which collect unit operating noise and combine machine learning models to determine the degree of cavitation and wear.
The oil analysis of the main transformer and generator set is also suitable for AI intervention. Dissolved Gas in Transformer Oil (DGA) data is a classic indicator for determining internal faults, traditionally relying on manual experience to compare thresholds. Machine learning models can learn complex mapping relationships between various gas components and fault types, providing more accurate discrimination for early faults such as overheating and discharge.
The direct benefits brought by predictive maintenance are considerable economic benefits: reducing unplanned downtime, extending maintenance intervals, and reducing spare parts inventory. More importantly, it brings' condition based maintenance 'from concept to reality - maintenance plans are driven by data rather than calendar.
##Reservoir Optimization Operation: AI Decision making in Multi Objective Game Theory
Reservoir scheduling is the core challenge in the operation of hydropower stations. The scheduling objectives often conflict with each other: maximizing power generation, ensuring flood control safety, guaranteeing downstream ecological flow, irrigation water supply, shipping demand... In the context of uncertain incoming water, finding a scheduling plan that takes into account multiple objectives is essentially a high-dimensional dynamic optimization problem.
Traditional methods rely on scheduling rule curves and manual experience, and lack adaptability when facing extreme scenarios of water scarcity or abundance. The introduction of AI technology provides a new solution path. Runoff forecasting is a prerequisite for scheduling. Based on meteorological forecast data and historical runoff data, machine learning models such as random forests, gradient boosting trees, and deep learning sequence models can provide predictions of incoming runoff for the next few days to months, providing a longer lead time for scheduling decisions.
In terms of optimization solutions, reinforcement learning has received widespread attention in recent years. Taking the reservoir state (water level, inflow, output) as the environmental state, taking power generation decisions as actions, and using power generation benefits, flood control risks, etc. as reward functions, intelligent agents can learn robust scheduling strategies in a large number of simulated scenarios. Compared to traditional dynamic programming methods, reinforcement learning is more flexible in handling high-dimensional state spaces and nonlinear constraints, and has fast online inference speed after training, which can support real-time scheduling.
Digital twin technology provides a carrier for visualization and simulation verification. By constructing a digital twin model of the reservoir basin and power plant units, dispatchers can simulate the effects of different scheduling schemes in a virtual environment, and then execute them selectively, reducing the cost of decision-making trial and error.
##Dam safety monitoring: AI guarding the 'pot of water above'
Dam safety is the bottom line of hydropower operation. Modern dams commonly deploy monitoring instruments for seepage, deformation, stress, water level, etc., accumulating a large amount of time-series data. The main applications of AI in the field of dam safety include: dam deformation prediction models based on monitoring data, learning deformation trends through historical displacement data, and early warning of abnormal deformation; Abnormal detection of seepage data, identifying sudden changes in seepage flow that may indicate potential hazards; And combining monitoring data with finite element simulation to achieve comprehensive evaluation of structural states.
With the popularization of satellite positioning technologies such as Beidou and GNSS, the accuracy and frequency of dam surface displacement monitoring have been greatly improved, providing a richer data foundation for AI models.
##Challenges and Prospects
The implementation of AI in the hydropower industry still faces several challenges. One is the problem of data silos: data between monitoring systems, production management systems, and meteorological systems is often stored in a scattered manner with different standards, and connecting them requires long-term investment. The second challenge is the small sample dilemma: there is a scarcity of fault samples in hydropower stations, and the training data for fault diagnosis models is insufficient, requiring the use of transfer learning, digital twin simulation data augmentation, and other methods. The third is model interpretability: the responsibility for scheduling and operation decisions is significant, and black box models are difficult to gain the complete trust of operators. Explainable AI (XAI) will be an important research direction.
Looking ahead, the integration of AI and hydropower stations will evolve along the path of "single point intelligence system intelligence watershed intelligence". From fault diagnosis of a single unit, to intelligent operation of the entire plant, and then to cascade power generation in the basin
【参考来源】本文内容综合整理自水电行业公开技术资料与人工智能在工业设备维护领域的公开发表研究。