In recent times, much attention has been paid to small hydropower plants (SHPs) with variable speed operation and different control techniques. Control complexity in SHPs is mainly caused by specific steady-state features of the water turbine, the long time constants of the hydraulic system and significant seasonal and/or aging-related deterioration in the system performance. This paper presents the most important features of the turbine model from a control point of view. It classifies control techniques for SHPs with variable speed operation in terms of the turbine type and SHP function (run-of-the-river and reservoir). Furthermore, various control methods are analysed taking into account the complexity of the method, dynamics, adaptability and applicability. The novelty of this study is the proposal of a simple, universal analytical formula, which, based on the basic dimensions of the turbine, determines the optimal operating curve. The proposed formula is verified on a real SHP 150 kW by comparison with measurements in the form of operational characteristics. The analysis of the annual energy production confirms the effectiveness of the approximation precision, yielding only 1% production losses, and shows an advantage of variable speed over constant speed in annual energy production of 16%.

Modern industries encourages the use of hydrogen as an energy carrier to decarbonize the electricity grid, Polymeric Electrolyte membrane fuel cell which uses hydrogen as a fuel to produce electricity, is an efficient and reliable ‘power to gas’ technology. However, a key issue obstructing the advancement of PEMFCs is the unpredictability of their performance and failure events caused by flooding and dehydration. The accurate prediction of these two events is required to avoid any catastrophic failure in the cell. A typical approach used to predict failure modes relies on modeling failure-induced performance losses and monitoring the voltage of a cell. Data-driven machine learning models must be developed to address these challenges. Herein, we present a machine learning model for the prediction of the failure modes of operating cells. The model predicted the relative humidity of a cell by considering the cell voltage and current density as the input parameters. Advanced regression techniques, such as support vector machine, decision tree regression, random forest regression and artificial neural network, were used to improve the predictions. Features related to the model were derived from cell polarization data. The model’s results were validated with real-time test data obtained from the cell. The statistical machine learning models accurately provided information on the flooding- and dehydration-induced failure events.

This paper presents experimental determination of the heat transfer coefficient and the friction factor in an internally rifled tube. The experiment was carried out on a laboratory stand constructed in the Department of Energy of the Cracow University of Technology. The tested tube is used in a Polish power plant in a supercritical circulating fluidized bed (CFB) boiler with the power capacity of 460 MW. Local heat transfer coefficients were determined for Reynolds numbers included in the range from ~6000 to ~50,000, and for three levels of the heating element power. Using the obtained experimental data, a relation was developed that makes it possible to determine the dimensionless Chilton–Colburn factor. The friction factor was also determined as a function of the Reynolds number ranging from 20,000 to 90,000, and a new correlation was developed that represents the friction factor in internally ribbed tubes. The local heat transfer coefficient and the friction factor obtained during the testing were compared with the CFD modelling results. The modelling was performed using the Ansys Workbench application. The k-ω, the k-ε and the transition SST (Share Stress Transport) turbulence models were applied.

In this study are presented results of numerical analysis of the thermal conditions of smooth waterwall tubes connected by fins and heated with equal heat flux. Inside tubes flows the water at supercritical parameters. The thermodynamic parameters at the inlet to each tube are similar but the mass flow of the fluid differs in each tube. Numerical model used for analysis allows to determine the temperature distribution, in online mode, in every control element of the analysed tubes and in the finite elements of the working fluid depending on the current thermodynamic parameters of the fluid and tubes. After calculation of the temperature distribution field, it can be used to determine the thermal stresses distribution in tubes and fins and determine the parts of elements, where the maximum values of the thermal stresses occur. The presented analysis was carried out for comparison of the thermal stresses distribution in small fragment of the combustion chamber wall in case of changing the fluid mass flows in neighbouring waterwall tubes.