The present invention provides a hydraulic turbine cavitation acoustic signal identification method based on big data machine learning. According to the method, time sequence clustering based on multiple operating conditions under the multi-output condition of the hydraulic turbine set is performed by utilizing an neural network, characteristic quantities of the hydraulic turbine set under a steady condition in a healthy state is screened; a random forest algorithm is introduced to perform feature screening of multiple measuring points under steady-state operation of the hydraulic turbine set, optimal feature measuring points and optimal feature subsets are extracted, finally a health state prediction model is constructed by using gated recurrent units; whether incipient cavitation is present in the equipment is judged. The present invention can effectively identify the occurrence of incipient cavitation in the hydraulic turbine set, reducing unnecessary shutdown of the equipment and prolonging the service life.

The present invention belongs to the technical field of ocean energy utilization, and provides a test system for turbine comprehensive performance of a pneumatic wave energy converter. The test system for turbine comprehensive performance of the pneumatic wave energy converter comprises a wave surface simulation system, an airflow rectifier system, a turbine device, a tabletop support structure, an instrument support frame and a sensor analysis system. A programmable linear motor is used to drive a bellows to simulate the complex oscillating airflow of the pneumatic wave energy converter; a complete sensor and measurement system is configured for various performance indexes of a turbine system; and finally, an effective technical evaluation means for the power generation efficiency and comprehensive performance evaluation of the pneumatic wave energy converter is provided. The present invention has simple assembly and disassembly technologies and high flexibility.

The rotating machine, comprises a shaft and at least one device for measuring at least the accelerations of the shaft along pitch, yaw and roll axes of the shaft or the angular position of the shaft around the pitch, yaw and roll axes, the or each device being mounted on the shaft

A turbomachine module with a longitudinal axis, the module comprising: a rotary casing rotatable around the longitudinal axis and arranged to carry a propeller provided with a plurality of blades; a system for changing the pitch of the blades of the propeller comprising: a control, and a mechanism for varying the pitch of the blades of the propeller connecting these blades to the control, wherein the control comprises a rotary actuator comprising a control body and a reference body which is integral with the rotary casing, and wherein the mechanism for varying the pitch comprises a synchronization ring which is driven in rotation around the longitudinal axis by the control body and which is guided in rotation on the rotary casing, the synchronization ring being connected to the blades.

This document discloses a solution for analysing cavitation in a hydro turbine of a hydroelectric power plant. According to an aspect, a method comprises: measuring, by using at least one motion sensor coupled to a rotor of the hydro turbine, motion of the rotor during operation of the hydro turbine and thereby generating measurement data; acquiring, by at least one processor, the measurement data and one or more operational parameters of the hydro turbine employed during said measuring, the one or more operational parameters indicative of power supply of the hydro turbine; computing, by the at least one processor on the basis of the measurement data and the operational parameters, at least one metric indicative of an effect of the cavitation on the hydro turbine; comparing, by the at least one processor, the at least one metric with at least one threshold and determining, on the basis of the comparison, that the effect of the cavitation is too high; and outputting, by the at least one processor on the basis of said determining, information indicating unsuitability of the one or more operational parameters.

A hydraulic machine has a runner, a guide apparatus, a draft tube, and an apparatus for measuring the water level in the region of the draft tube. The apparatus includes a first pressure measuring device, a second pressure measuring device, and a unit for generating differential pressure values. The pressure measuring devices are arranged in such a way that they may detect the pressures applied inside the draft tube and are respectively connected to the unit. The first pressure measuring device is located above the second pressure measuring device, and the unit is configured to generate the difference between the pressures that the pressure measuring devices detect. There is also described a method for dewatering the runner of such a hydraulic machine.

The present invention provides a hydraulic turbine cavitation acoustic signal identification method based on big data machine learning. According to the method, time sequence clustering based on multiple operating conditions under the multi-output condition of the hydraulic turbine set is performed by utilizing an neural network, characteristic quantities of the hydraulic turbine set under a steady condition in a healthy state is screened; a random forest algorithm is introduced to perform feature screening of multiple measuring points under steady-state operation of the hydraulic turbine set, optimal feature measuring points and optimal feature subsets are extracted, finally a health state prediction model is constructed by using gated recurrent units; whether incipient cavitation is present in the equipment is judged. The present invention can effectively identify the occurrence of incipient cavitation in the hydraulic turbine set, reducing unnecessary shutdown of the equipment and prolonging the service life.

An ocean tidal current energy power generating system, including a fixing mechanism, an ocean tidal current energy power generator set and a signal monitoring mechanism. The fixing mechanism includes floating bodies, fixing rods, horizontal supporting rods, and a working platform; the floating bodies are fixed to seabed by means of anchor chains; the fixing rods are fixed to the floating bodies; the horizontal supporting rods and the working platform are respectively fixed to underwater portions of the fixing rods and overwater portions of the fixing rods. The power generator set includes underwater assemblies and an overwater assembly. Each underwater assembly includes blades, a hub, a main shaft, a gear box, a coupling, a power generator, a stern cabin and a yawing mechanism, successively connected to each other; a variable pitch mechanism is disposed in the hub.

A system includes a water turbine, a plurality of positioning winches coupled to the water turbine and a plurality of positioning cables. An individual positioning cable extends between a fixed point at a first end and the water turbine at a second end and is coupled to a corresponding positioning winch that is configured to extend and retract the individual positioning cable between the fixed point and the water turbine. A plurality of sensors is configured to sense water conditions around the water turbine. A position control system is connected to the plurality of positioning winches and connected to the plurality of sensors. The position control system is configured to position the water turbine using the plurality of positioning winches according to the water conditions sensed by the plurality of sensors.

The invention provides a system for the extraction of energy from an underwaterflow stream in a body of water. The system comprises a support frame which comprises a plurality of receptacles for mounting functional modules on the support frame. The plurality of receptacles are arranged to form a horizontally-distributed two-dimensional array on the support frame. The plurality of vertical axis turbine units is configured to be interchangeably mounted in the receptacles.