Wind driven generator fault diagnosis based on long-term and short-term memory model recurrent neural network

A cyclic neural network, wind turbine technology, applied in biological neural network models, computational models, neural architectures, etc., can solve problems such as limited accuracy of wind power fault detection

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Problems solved by technology

Therefore, SVM has limited accuracy for wind fault detection

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  • Wind driven generator fault diagnosis based on long-term and short-term memory model recurrent neural network
  • Wind driven generator fault diagnosis based on long-term and short-term memory model recurrent neural network
  • Wind driven generator fault diagnosis based on long-term and short-term memory model recurrent neural network

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Embodiment Construction

[0072] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0073] The wind turbine fault diagnosis based on the long short-term memory model recurrent neural network includes the following steps:

[0074] Step 1: Model the wind turbine benchmark model, subdivide the wind turbine benchmark system into eight types of faults, and set the fault type and occurrence time.

[0075] Said step 1 further includes:

[0076] Step 1.1: See figure 1 , the wind turbine benchmark model includes pitch system, transmission system, generator and frequency converter system and controller.

[0077] The controller references β by using the blade pitch angle r to control the pitch system, by using the generator torque reference τ g，r To control the generator and inverter system. P r For reference power, the value is 4.8×10 6 ;

[0078] Vw represents the wind speed, passing through the pitch system, the blades of the pitch system ro...

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Abstract

The invention relates to a wind driven generator fault diagnosis method based on a long-term and short-term memory model recurrent neural network. The method comprises the following steps of: 1, carrying out the modeling of a fan reference system, enabling the fan reference system to be subdivided into eight types of faults, and setting the fault types and occurrence time, 2, simulating the fan reference model to obtain an actual measurement value, 3, performing data preprocessing on the actual measurement value obtained in the step 2, and constructing a data sample set of the actual measurement value, and 4, building an LSTM model, training the preprocessed data in the step 3 by using the LSTM model, continuously adjusting parameters in a training process, evaluating a training effect byusing an equipartition error, comparing a predicted value with the actual measurement value of the sensor obtained in the step 2, and setting a threshold value to judge the fault occurrence time and position.

Description

technical field [0001] The invention relates to the field of fault detection of wind power generators, in particular to a fault diagnosis of wind power generators based on a long-short-term memory model cycle neural network. Background technique [0002] Reference [1] uses support vector machine (SVM) for fault detection and isolation of a variable speed horizontal axis wind turbine consisting of three blades and a full converter. The SVM approach is data-based and thus robust to processing knowledge. Furthermore, it is based on structural risk minimization, which enhances generality and allows consideration of process nonlinearities through the use of flexible kernels. In this work, radial basis functions are used as kernels. Different parts of the process are studied, including actuators, sensors and process failures. With dual sensors, we can quickly detect sensor faults for blade pitch position, generator and rotor speed (2 sampling periods for fixed value faults), bu...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06N3/04G06N3/08G06N20/00G06F113/06G06F119/14
CPCG06N3/08G06N20/00G06N3/044G06N3/045Y04S10/50
Inventor 滕婧杨韬燃李常玲冯一展
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