A hydroelectric power generation system includes a water turbine, a generator connected to the water turbine, and a controller. The water turbine is arranged in a flow path through which a fluid flows. The controller performs a pressure control by controlling the generator to regulate a pressure of the fluid downstream of the water turbine. The pressure control includes a first control regulating the pressure in parallel with a regenerative operation of the generator, and a second control regulating the pressure in parallel with a power running operation of the generator.

A method for controlling wind turbines. Incident signal data is obtained from wind turbines and fed to an artificial intelligence (AI) model in order to identify patterns in the incident signals generated by the wind turbines. One or more actions are associated to the identified patterns, based on identified actions performed by the wind turbines in response to the generated incident signals. During operation of the wind turbines, one or more incident signals from one or more wind turbines are detected and compared to patterns identified by the AI model. In the case that the detected incident signal(s) match(es) at least one of the identified patterns, the wind turbine(s) are controlled by performing the action(s) associated with the matching pattern(s).

A system for generating power includes an environmental engine operating on one or more computing devices that determines a wind flowing over a blade of a wind turbine, wherein the wind flowing over the blade of the wind turbine varies based on environmental conditions and operating parameters of the wind turbine. The system also includes an artificial intelligence (AI) ensemble engine operating on the one or more computing devices that generates a plurality of different models for the wind turbine. Each model characterizes a relationship between at least two of a rotor speed, a blade pitch, the wind flowing over the blade, a wind speed and a turbulence intensity for the wind turbine. The AI ensemble engine selects a model with a highest efficiency metric, and simulates execution of the selected model to determine recommended operating parameters.

A method includes receiving, via one or more turbine-level controllers, an indication of at least one of a communication loss between the one or more turbine-level controllers and a farm-level controller, a detection of an absence of reactive power regulation by the farm-level controller, or a reactive power command of the farm-level controller being equal to or above a saturation threshold during transitioning between a baseline operational mode and reactive power mode, the reactive power mode being characterized in that only reactive power is generate. Upon receipt of the indication, the method includes adjusting a reactive power response of one or more reactive power regulators of the one or more turbine-level controllers so as to avoid an overshoot reactive power event or an undershoot reactive power event at the point of interconnection.

The invention provides a wind turbine method that includes receiving alarm state data indicating that the wind turbine has entered an alarm state in which operation of the wind turbine has stopped, and receiving sensor data from a plurality of sensors of the wind turbine indicative of operating conditions associated with the wind turbine. When the alarm state data is received, the method includes executing a trained machine learning model based on the received sensor data and the alarm state to obtain an output, where the machine learning model is trained based on historical data associated with a plurality of wind turbines, the historical data being indicative of the plurality of wind turbines previously being in the alarm state. The method includes providing, based on the obtained output, an action to be performed to allow the wind turbine to resume operation.

A method for operating a wind turbine includes determining at least one wind condition of the wind turbine for a plurality of time intervals. The method also includes determining a status of the wind turbine at the beginning of each of the plurality of time intervals. Further, the method includes determining at least one vibration parameter of the wind turbine for one or more preceding time intervals of the plurality of time intervals. Moreover, the method includes predicting whether a trip event is imminent based on the at least one wind condition, the status of the wind turbine at the beginning of each of the plurality of time intervals, and the vibration parameter. Thus, the method further includes implementing a control action for the wind turbine so as to prevent the trip event.

The disclosure relates to a compressor for an exhaust gas turbocharger. The compressor includes a compressor wheel, a compressor housing having a volute and a diffuser, and a device for modifying the diffuser width. The device for modifying the diffuser is configured to automatically alter the diffuser width as a function of one or more current operating parameters of the compressor.

The present invention provides a system and method for converting wave energy into electric energy in an intelligent, practical, and efficient manner. The system utilizes a power input shaft coupled with a vertically reciprocating buoy to rotate a crank gear and a ratchet gear meshing therewith. An intelligent control system is included to monitor, control, and optimize the operations of the system. The length of the power input shaft is adjusted in response to water level fluctuations so that the rotational motion of the crank gear is intelligently controlled within a predetermined desirable region for maximum efficiency.

An apparatus energized by a flowing fluid includes at least one turbine blade having a trailing edge angle and an elastic deformation member connected to the at least one turbine blade. The deformation of the elastic deformation member changes an orientation of a trailing edge angle of the at least one turbine blade.

A system for generating power includes an environmental engine that determines performance metrics for a plurality of wind turbines deployed at a plurality of windfarms, such that each windfarm includes a corresponding subset of the plurality of windfarms. The performance metrics for a given wind turbine of the plurality of wind turbines characterizes wind flowing over blades of the given wind turbine. The system includes an artificial intelligence (AI) ensemble engine operating on the one or more computing devices that generates a set of models for each wind turbine of the plurality of wind turbines, wherein each model of each set of models is generated with a different machine learning algorithm and selects, for each respective set of models, a model with a highest efficiency metric. The AI engine provides edge computing systems operating at the plurality of windfarms with a selected model and corresponding recommended operating parameters.