With the growth in the share of variable renewable energy sources, fluctuations in the power generation caused by these types of power plants can diminish the stability and flexibility of the grid. These two can be enhanced by applying frequency containment using hydropower plants as an operational reserve. The frequency containment in hydropower plants is automatically controlled by speed governors within seconds. Disturbances such as fluctuations in the net head and aging may diminish the performance of the controllers of the speed governors. In this study, model reference adaptive control approaches based on the Massachusetts Institute of Technology (MIT) rule and Lyapunov method were exploited in order to improve the performance of the speed governor for frequency containment control. The active power control with frequency control was enhanced by the aforementioned adaptive control methods. A mathematical model of a hydropower plant with a surge tank and medium penstock was constructed and validated through site measurements of a plant. It was shown that, as they are applicable in real life, both methods perform significantly better compared to conventional proportional-integrator control. Even in first five deviations, the performance of the conventional controller improved by 58.8% using the MIT rule and by 65.9% using the Lyapunov method. When the two adaptive control approaches were compared with each other, the MIT rule outputted better results than the Lyapunov method when the disturbance frequency was higher; however, the latter was more functional for rare disturbances.

Hydroelectric power plants (HEPPs) are renewable energy power plants with the highest installed power in the world. The control systems are responsible for stopping the relevant unit safely in case of any malfunction while ensuring the desired operating point. Conventional control systems detect anomalies at certain limits or predefined threshold values by evaluating analog signals regardless of differences caused by operating conditions. In this study, using real data from a large hydro unit (>150 MW), a normal behavior model of a hydraulic governor’s oil circulation in an operational HEPP is created using several machine learning methods and historical data obtained from the HEPP’s SCADA system. Model outputs resulted in up to 96.45% success of prediction with less than 1% absolute deviation from actual measurements and an R² score of 0.985 with the random forest regression method. This novel approach makes the model outputs far more appropriate to use as an active threshold value changing for different operating conditions, eliminating insufficiency of the constant threshold, resulting in the detection of anomalies in early stages and taking precautions accordingly. The study fills the gap in the literature on anomaly detection in hydraulic power units, which is a demanding task with state-of-the-art methods.

Background: Parameters of the hydroelectric power plant controllers are typically tuned at the nominal operating conditions such as nominal head and single unit operation. Water level variations in reservoir and/or tailwater, and the presence of other active units sharing the penstock are common disturbances to the nominal assumption. Methods: This article proposes two adaptive add-ons, namely gain scheduling and model reference adaptive control, to the existing speed controllers to improve grid synchronization performance when the site conditions are not nominal. The add-ons were designed and tested on a validated dynamic model of a power plant unit by using a software-in-the-loop simulation setup. An off-season scenario is simulated, in which the original controller of the unit cannot bring the turbine to synchronize with the grid due to low gross head. Then, the add-ons were implemented on-site and experiments were performed under similar conditions. The parameter sets used in gain scheduling for different operation bands are determined off-line with the help of operational experience. The model reference adaptive control add-on requires a reference model and a learning rate. A description of the turbine speed-up profile at nominal operating conditions is sufficient to be used as the reference model. The proposed piecewise linear reference model favors stability over speed in settling to the nominal speed. Results: It is experimentally shown that the proposed add-ons compensate the negative effect of head loss in grid synchronization, and perform similar to the ideal performance at the nominal head. Conclusion: Both add-ons can be implemented on the available off-the-shelf speed governor controllers. They are suitable for use in all hydroelectric power plants, especially in unmanned ones, for automatic synchronization with less waste water.

This research was conducted to evaluate and compare the efficiency of the modern external type thrust bearing cooling system (TBCS) with plate-type heat exchangers (PTHEs) applied as an alternative to standard design external type TBCS with shell-and-tube heat exchangers (STHEs) in a 180 MW large hydro power plant by experimental methods. Although similar studies are available in the literature, there is no comprehensive study on the effects of different parameters on performance and other plant parameters. The main parameters examined in the study are the cooling rate, oil temperature difference, average pad temperature (APT), and generator winding temperature. The tests were carried out over the range of 144–150.1 MW unit loads, 580–1317 L/min water flow rates, and 998–1411 L/min oil circulation flow rates. The results showed that the APT can only be reduced up to 73.4 °C at 1411 L/min oil circulation flow rate by 252.6 kW cooling, the optimum oil circulation flow rate is 1195 L/min, APT can be reduced by 1.7 °C and the maximum winding temperature by 1.3 °C when external type TBCS with PTHEs is used, and structural changes must be made in the thrust bearing design to provide further decrease in pad temperature.

Hydropower is facing new challenges due to the increasing need for flexibility, environmental compliance and integration into modern energy systems. In this context, innovation and digitalization have become key enablers for improving the performance and efficient role of hydropower plants. This chapter focuses on technological evolution through innovation and digitalization as supported by the literature and activities outcome of Working Group 2 (WG2) within the PEN@Hydropower COST Action. The chapter follows a holistic approach, considering different perspectives such as plant operation, system integration, environmental impacts and stakeholder needs. Key topics include digital twins, data-driven monitoring, predictive maintenance, new designs of components and system concepts. In addition, the interaction between hydropower and the water-energy-environment nexus is discussed, with examples related to sediment management, ecological flows and hybrid systems. Finally, current limitations and future directions are outlined, highlighting the need for practical, scalable and integrated solutions in hydropower.