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26790 results about "Wind power" patented technology

Wind power or wind energy is the use of wind to provide the mechanical power through wind turbines to turn electric generators and traditionally to do other work, like milling or pumping. Wind power is a sustainable and renewable energy, and has a much smaller impact on the environment compared to burning fossil fuels.

Wind turbine

InactiveUS7042109B2Improve efficiencyCost per unit power generationWindingsWind motor controlRotational energyAir core
A wind turbine for generating electrical power from wind energy includes a turbine rotor mounted for rotation in wind, and having multiple blades for converting energy in the wind into rotational energy. A generator is coupled with said turbine rotor such that said turbine rotor drives said generator. The generator has a stationary air core armature that is located in a magnetic airgap between two generator rotor portions. The generator rotor portions have circumferential arrays of multiple alternating polarity permanent magnets attached to ferromagnetic back irons such that the permanent magnets drive magnetic flux back and forth between each rotor portion and through the stationary air core armature. The stationary air core armature has multiple phase windings of multiple individually insulated strand conductor wire that is wound with two separate portions including an active length portion and an end turn portion. The end turn portion is located outside the magnetic airgap and traverses predominately circumferentially, and the active length portion is located in the magnetic airgap and traverses predominately non-circumferentially and perpendicular to the direction of the magnetic airgap. The end turn portion has a thickness that is greater than the thickness of said active length portion in the direction of said magnetic airgap. AC voltage is induced in the multiple phase windings as the turbine rotor rotates.
Owner:MARIAH POWER

Combined wind power prediction method suitable for distributed wind power plant

The invention provides a combined wind power prediction method suitable for a distributed wind power plant. The method comprises the following steps: step 1, acquiring data and pre-processing; step 2, utilizing a training sample set and a prediction sample set which are normalized to build a wind speed prediction model based on a radial basis function neural network and predict the wind speed and variation trend of distribution fans at the next moment; step 3, building a distributed wind power plant area CFD (computational fluid dynamics) model and externally deducing the prediction wind speed of each fan in the plant area according to factors such as the terrain, coarseness and wake current influence of a distributed wind field; step 4, acquiring the power data of an SCADA (supervisory control and data acquisition) system fan of the distributed wind field; and step 5, adopting correlation coefficients. The invention firstly provides a double-layer combined neural network to respectively predict the wind speed and power. Models are respectively built through adopting appropriate efficient neural network types, and improved particle swarm optimization with ideas of 'improvement', 'variation' and 'elimination' is additionally added to optimize the neural network, so that the speed and precision of modeling can be effectively improved, and the decoupling between wind speed and power is realized.
Owner:LIAONING ELECTRIC POWER COMPANY LIMITED POWER SCI RES INSTION +2

Supplying, dividing and feeding devices, cooking equipment and feeding method thereof

According to the embodiment, the invention relates to the technical field of smart home, and discloses a feeding device of cooking equipment and the cooking equipment. The feeding device comprises a dividing device and a supplying device, wherein the dividing device adopts a worm or a disc provided with a charging groove; cooking raw materials in a storage box are supplied to a supplying device; and the dividing device and the supplying device are separated when the cooking raw materials are weighed. According to the supplying device, the cooking raw materials are conveyed into a pot body of the cooking equipment by virtue of a wind power device/vacuum device, or by virtue of a horizontal worm in a horizontal conduit and through self-gravity of the cooking raw materials, so that automatic feeding of the cooking equipment is achieved and workload of a user is reduced; a multipurpose mating device is provided for a plurality of intelligent home products, and a foundation is created for intelligent kitchen appliances or the smart home. The dividing device is applicable to such intelligent cooking equipment as an electric cooker, a bread maker, a frying machine, a soybean milk making machine, a bean curd machine, a stirrer, a coffee machine or a tea maker and the like.
Owner:李亚锐

Wind power forecasting method based on genetic algorithm optimization BP neural network

The invention discloses a wind power forecasting method based on a genetic algorithm optimization BP neural network, comprising the steps: acquiring forecasting reference data from a data processing module of a wind power forecasting system; establishing a forecasting model of the BP neural network to the reference data, adopting a plurality of population codes corresponding to different structures of the BP neural network, encoding the weight number and threshold of the neural network by every population to generate individuals with different lengths, evolving and optimizing every population by using selection, intersection and variation operations of the genetic algorithm, and finally judging convergence conditions and selecting optimal individual; then initiating the neural network, further training the network by using momentum BP algorithm with variable learning rate till up to convergence, forecasting wind power by using the network; and finally, repeatedly using a forecasted valve to carry out a plurality of times of forecasting in a circle of forecast for realizing multi-step forecasting with spacing time interval. In the invention, the forecasting precision is improved, the calculation time is decreased, and the stability is enhanced.
Owner:SOUTH CHINA UNIV OF TECH +1
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