Wind power variable pitch system fault diagnosis method based on CEEMDAN-BNs

A technology of system faults and diagnostic methods, which is applied in wind turbines, monitoring of wind turbines, complex mathematical operations, etc., can solve the complex establishment of Bayesian networks, the limitations of Bayesian network applications, and the acquisition of conditional probability distributions of Bayesian networks Difficulty and other problems, to achieve the effect of improving endpoint effects, improving modal aliasing, and reducing training time

A technology of system faults and diagnostic methods, which is applied in wind turbines, monitoring of wind turbines, complex mathematical operations, etc., can solve the complex establishment of Bayesian networks, the limitations of Bayesian network applications, and the acquisition of conditional probability distributions of Bayesian networks Difficulty and other problems, to achieve the effect of improving endpoint effects, improving modal aliasing, and reducing training time

CN114004091AActive Publication Date: 2022-02-01LANZHOU UNIVERSITY OF TECHNOLOGY

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  • Wind power variable pitch system fault diagnosis method based on CEEMDAN-BNs
  • Wind power variable pitch system fault diagnosis method based on CEEMDAN-BNs
  • Wind power variable pitch system fault diagnosis method based on CEEMDAN-BNs

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Embodiment

[0069] Such as figure 1 Shown, a kind of wind power variable pitch system fault diagnosis method based on CEEMDAN-BNs of the present invention comprises the following steps:

[0070] S1. Add white noise to the sample time series signal of the fault state, perform CEEMDAN modal decomposition, and obtain IMF and margin signals of multiple modal component signals.

[0071] The simulation model of the wind turbine is monitored, the vibration signal in the fault state is collected, the collected signal is divided and preprocessed, and the sample time series signal of the fault state is obtained.

[0072] Adaptive Complete Empirical Mode Decomposition of Noise (CEEMDAN), as an adaptive and completely non-recursive time-frequency analysis method, can decompose multi-component signals into multiple single-component signals at one time, and use iterative search to adaptively match when solving The center frequency and effective bandwidth of each single-component signal effectively sup...

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Abstract

The invention provides a wind power variable pitch system fault diagnosis method based on CEEMDAN-BNs, and the method comprises the steps of: adding white noise into a sample time sequence signal in a fault state, and carrying out CEEMDAN mode decomposition, thus obtaining IMF and margin signals of a plurality of mode component signals; selecting a mode component with a high signal-to-noise ratio and a large correlation coefficient in the IMF signal, and constructing an energy characteristic matrix through Hilbert transform; performing interval division on the energy characteristic matrix; constructing a Bayesian network topological structure according to the divided energy characteristic matrix and expert priori knowledge; searching an optimal network topology structure of the Bayesian network through a hill climbing algorithm; and training the Bayesian network, and inputting the to-be-diagnosed signal into the trained Bayesian network to obtain a fault diagnosis result. According to the method, the Bayesian network is constructed through feature extraction in combination with the CEEMDAN algorithm, and while the accuracy is improved, simplification of the Bayesian network structure and improvement of the network training speed are realized.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis, in particular to a fault diagnosis method for a wind variable pitch system based on CEEMDAN-BNs. Background technique [0002] Wind turbines have the characteristics of huge equipment, high cost, and complex structure, and are generally located in relatively remote areas such as Gobi desert, plateau, or sea. Therefore, once a subsystem is damaged and fails during operation, it cannot be repaired in time. This will lead to aggravated faults, and may even lead to failures of adjacent systems, resulting in large economic losses. Therefore, fault diagnosis and status detection of wind turbines are very important for daily operation and maintenance. The wind variable pitch system is an important subsystem to maintain the safe and stable operation of wind turbines. It is one of the high-frequency fault components of wind turbines. It ensures the safe operation of wind turbines by adjusting the ...

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Application Information

Patent Timeline
01 Feb 2022
Publication
CN114004091A
IPC
G06F30/20; G06K9/62; G06F17/14; F03D17/00; G06F119/10
CPC
G06F30/20; G06F17/14; F03D17/00; G06F2119/10; G06F18/214; G06F18/29; Y02E10/72
Inventors
ηŽ‹θΏ›θŠ±; ηŽ‹θ·ƒιΎ™