Abstract:
A method for controlling cooling system of a power equipment and a system using the same. The method includes steps of: obtaining a first data set representing operational cost related parameters specific to the power equipment and its cooling system at a series of time intervals of a first load cycle in a history profile; obtaining a second data set representing operational cost related parameters specific to the power equipment and its cooling system at a series of time intervals of a second load cycle in the history profile, where the second load cycle follows the first load cycle; in consideration of the parameters represented by the first data set, through knowledge-based predetermined numerical and/or logical linkages, establishing a third data set representing optimal cooling capacity parameters for the cooling system at the series of time intervals of the first load cycle according to criteria for operational cost optimization of the power equipment; in consideration of the parameters represented by the second data set, through knowledge-based predetermined numerical and/or logical linkages, establishing a fourth data set representing optimal cooling capacity parameters for the cooling system at the series of time intervals of the second load cycle according to criteria for operational cost optimization of the power equipment; establishing a fifth data set representing a cooling capacity parameter difference between the established cooling capacity parameters concerning the first load cycle and the second load cycle; establishing a sixth data set representing cooling capacity parameters for the cooling system at a series of time intervals of a present load cycle by computationally correcting the established cooling capacity parameter concerning the time intervals of the second cycle load with use of the cooling capacity parameter difference; and controlling the cooling system to operate at a series of time intervals of the present load cycle at the stablished cooling capacity parameters concerning the present load cycle represented by the sixth data set.
Abstract:
One embodiment of the present application provides a method for obtaining a first data set representing operational cost related parameters specific to the power equipment and its cooling system forecasted for a series of time intervals of present load cycle in consideration of a second data set representing operational condition related parameters for the power equipment forecasted for a series of time intervals of present load cycle; in consideration of the parameters represented by the first data set, through knowledge-based predetermined numerical and/or logical linkages, establishing a third data set representing cooling capacity parameters for the cooling system at the series of time intervals of the present load cycle according to criteria for operational cost optimization of the power equipment and its cooling system for the present load cycle; and in the present load cycle, controlling the cooling system to operate at the cooling capacity parameters at the series of time intervals represented by the established third data set.
Abstract:
The present application includes wind turbine condition monitoring method and system. The method comprises: acquiring historical SCADA data, and wind turbine reports corresponding to the historical SCADA data; training an overall model for overall diagnosing the wind turbine, and training different individual models for analyzing different components of the wind turbine based on the historical SCADA data and the corresponding wind turbine report, by establishing relationship between the historical SCADA data and the wind turbine report; acquiring real time SCADA data, inputting the real time SCADA data to the trained overall model, obtaining the health condition of the wind turbine from the trained overall model, and performing individual diagnosing step if the trained overall model determines wind turbine as defective status; inputting the real time SCADA data to the trained individual model corresponding to the defective component, and obtaining the fault details of the defective component from the trained individual model corresponding to the defective component.
Abstract:
A control system for an electric vehicle charging station (EVCS) is provided, the control system comprises: a central controller configured to receive an ancillary service order from a power grid and distribute the ancillary service order to one or more local controllers periodically; and the one or more local controllers configured to control a plurality of electric vehicle supply devices based on the distributed ancillary service order in real time. The method controlling the electric vehicle charging station (EVCS) is also provided.