Nowadays, hydropower plants are of paramount importance for the integration of intermittent renewable energy sources in the power grid. In order to match the energy generated and consumed, Large Hydraulic Turbines have to work at off-design conditions, which may lead to dangerous unstable operating points involving the hydraulic, mechanical and electrical system. Under these conditions, the stability of the power grid and the safety of the powerplant itself can be compromised. For many Francis Turbines, one of these critical points, that usually limits the maximum output power, is the full load instability. Therefore, these machines usually work far away from this unstable point, reducing the effective operating range of the unit. In order to extend the operating range of the machine, working closer to this point with a reasonable safety margin, it is of paramount importance to monitor and to control relevant parameters of the unit, which have to be obtained with an accurate sensor acquisition strategy.In the frame of a large EU Project, field tests in a large Francis Turbine located in Canada (rated power 444 MW) have been performed. Many different sensors were used to monitor several working parameters of the unit for all its operating range. Particularly for these tests, more than 80 signals, including ten types of different sensors and several operating signals that define the operating point of the unit, were simultaneously acquired. The present study focuses on the optimization of the acquisition strategy, which includes type, number, location, acquisition frequency of the sensors and corresponding signal analysis to detect the full load instability and to prevent the unit from reaching this point. In this way, the operating limits of the unit can be more accurately defined and therefore the effective operating range increased.

Lead-cooled fast reactor (LFR) is an important reactor type of the Generation IV nuclear energy, and the main coolant pump (MCP) is the only and most critical power equipment in primary circuit of LFR. The shutdown idling performance of the MCP, is one of the most important safety indicators in the event of a station blackout accident. When the motor loses its power supply, the MCP needs to run under the inertia of the flywheel to ensure the coolant flow for a short time. This study mainly focuses on the idling mathematical model of the MCP under power failure accidents. In this study, taking the shutdown idling performance of the MCP as the optimization goal, the parametric optimization analysis of the impeller and diffuser structure of the MCP was carried out. Specifically, based on the platform ISIGHT, the software of CFturbo, TurboGrid, CFX, Matlab and FLOWMASTER are systematically integrated, the automated 3D modeling, automatic meshing and automatic CFD calculation of different hydraulic model of the MCP are realized. And the neural network mathematical model between the geometric parameters of MCP and the idling performance indicators is established. The study shows that there is a strong linear relationship between the idling performance of the MCP and its head and shaft power, and meridian parameters of Δβ 2, Z 1, Φ 1h and the radial surface parameters of Δβ 1 and Δβ 2, Z 1 and Φ 1h have the greatest impact on the idling performance of MCP.