Journal of Energy Technology

THEORETICAL AND EXPERIMENTAL INVESTIGATIONS OF A WATER HAMMER IN SAVA RIVER KAPLAN TURBINE HYDROPOWER PLANTS

2025-04-18T08:39:49+02:00

This paper deals with critical flow regimes that may induce an unacceptable water hammer in the Sava River Kaplan turbine hydropower plants. The rigid water hammer model is introduced first. The computational results are then compared with the results of measurements in two distinct hydropower plants (HPP): (i) The refurbished and upgraded Medvode HPP, and (ii) The newest Brežice HPP. Comparisons of the computed and measured results are examined for normal operating regimes. The water hammer in the two power plants is controlled by appropriate adjustment of the wicket gates and runner blades closing/opening manoeuvres. The agreement between the computed and measured results is reasonable.

ASSESSING THE EFFECTS OF A HYDROPOWER PLANT BASIN ON FISH SPAWNING IN AN UPSTREAM RIVER TRIBUTARY

2025-04-18T08:39:49+02:00

This paper presents a combined modeling approach to evaluate the ecological effects on the habitat of an upstream tributary of a river with a series of hydropower plants. The influence is investigated of the last planned hydropower plant to be built, which has a large impact on the river ecosystem. The new hydropower plant basin will affect the tributary with hydropeaking in the upstream basin. A simulation was conducted of spawning conditions for two protected fish species. The analysis combined a hydro-morphological model with a fish module that considers the water depth and velocity necessary for fish reproduction. The different river discharge scenarios were simulated, incorporating the hydropeaking effects of the new hydropower plant basin on the upstream tributary. With the new hydropower plant, sustainable measures are planned to prevent the damaging negative impacts that could lead to the degradation of the river ecosystem and the destruction of the existing ecosystem at the river’s confluence. The results indicate that, after the hydropower plant begins operation, the habitat`s suitability will decrease, and the planned sustainable measures will not provide a fully satisfactory solution.

A REVIEW OF ARTIFICIAL INTELLIGENCE IN NUCLEAR POWER PLANTS

2025-04-18T08:39:50+02:00

Nuclear power plants are recognised as complex systems, where maintenance is critical for ensuring safety and operational stability. Time-based preventive maintenance programmes are employed in most nuclear power plants, relying on periodic inspections to prevent equipment failures. However, this method is considered resource-intensive and not always efficient. An alternative is offered by Artificial Intelligence and condition-based maintenance, which allow early fault detection, reduce unnecessary maintenance tasks, and lower operational costs. The potential of Artificial Intelligence in nuclear power plants is vast, ranging from operational improvements to predictive maintenance. Techniques such as Supervised and Unsupervised Learning are highlighted as essential tools for fault detection, pattern recognition, and predictive modelling. In Supervised Learning, known input-output pairs are used to train models, while Unsupervised Learning is employed to identify hidden patterns in unlabelled data, which is particularly useful in the large, unstructured datasets found commonly in nuclear power plants. The challenges in integrating Artificial Intelligence into nuclear power plant operations shall be noted, including the lack of standardised procedures for selecting and applying algorithms. Despite these challenges, AI-driven tools, including Deep Learning and hybrid models, have shown promising results in fault detection and prediction in nuclear power plants. These advancements support the broader goal of improving safety and operational efficiency. In conclusion, although Artificial Intelligence has not yet been adopted fully across all nuclear power plants, it is seen as a promising advancement for the future of nuclear energy operations. Its implementation enhances fault detection, reduces operational risks, and ensures more reliable energy production.

FFA FW FLOW INFLUENCE AT NPP KRŠKO

2025-04-18T08:39:51+02:00

The Krško Nuclear Power Plant (NEK) operates based on a Pressurised Water Reactor (PWR), which utilises three loops for heat transfer: primary, secondary, and tertiary. Heat generation occurs in the primary loop; steam production takes place in the secondary loop; and waste heat is discharged in the tertiary loop. During outages, which occur every 18 months, the secondary systems are exposed to the atmosphere, increasing the risk of corrosion. To prevent this, in 2021, the plant used a chemical solution, Film Forming Amine (FFA), which formed a protective hydrophobic layer on the inner surfaces of the pipelines.

In March 2021, during the first use of FFA, deviations were observed in the main feedwater (FW) flow measurements. This affected the reactor power calculations, leading to a 0.4–0.5 % reduction in plant output (approximately 4 MWe). The main feedwater flow is a critical parameter for secondary calorimetric calculations, and has the largest impact on error in the event of deviations.

The power reduction was confirmed by comparing various process parameters, including changes in the primary loop temperature differences (ΔT), main steam flow (MS), and generator output vs. condenser vacuum. Since the measurement of the main feedwater flow contributes the most to the uncertainty of primary flow and reactor calorimetric calculations, NEK is focused on improving its accuracy.

Developing a numerical model in the computer-based programming environment is proposed as part of further research. This model would enable independent calculations of the main feedwater flow, to reduce the impact of the FFA chemicals on the measurement readout and its associated calculations. The model will be based on thermodynamic equations and algorithms for determining the flow with lower uncertainty than the current system. Using this model, correction factors should be obtained to adjust the current venturi meter readings. Ultimately, this approach will ensure better plant management, reduce energy losses, and increase revenues for NEK and its stakeholders.