Siemens Aktiengesellschaft

Jochen Cleve

Per Egedal

Peder Bay Enevoldsen

Hans-Georg Zimmermann

Kai Heesche

Ralph Grothmann

Christoph Tietz

Søren Hjort

Esther Meyer

Martin Greiner

Disclosed is a method for the computer-assisted modeling of a technical system. One or more output vectors are modeled dependent on one or more input vectors by the learning process of a neural network on the basis of training data of known input vectors and output vectors. Each output vector comprises one or more operating variables of the technical system, and each input vector comprises one or more input variables that influence the operating variable(s). The neural network is a feedforward network with an input layer, a plurality of hidden layers, and an output layer. The output layer comprises a plurality of output clusters, each of which consists of one or more output neurons, the plurality of output clusters corresponding to the plurality of hidden layers. Each output cluster describes the same output vector and is connected to another hidden layer.

Method for the computer-assisted modeling of a wind power installation or a photovoltaic installation with a feed forward neural network

A method and a device for operating a wind farm with a plurality of wind turbines are provided. According to the method, operating parameters of the wind turbines of the wind farm are adjusted according to an optimization goal, the optimization goal being the maximum value of the total output of the wind farm produced from the sum of all individual outputs of the wind turbines. The optimization goal differs from conventional optimization goals where the respective individual outputs of the wind turbines are optimized without taking the overall output into consideration.

Method for operating a wind farm comprising a plurality of wind turbines

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Munich, Georgia

Jochen Cleve