Fan blade early icing fault detection method based on deep neural network

Abstract

The invention relates to a fan blade early icing fault detection method based on a deep neural network pre-trained by an auto-encoder. The neural network comprises a fault feature mining and classification network, is used for solving the problem that the early icing state of a fan is difficult to detect in the prior art, and adopts the specific technical scheme that the method comprises the following steps: (1) acquiring a fan icing original data set; (2) preprocessing the original data set to obtain a training set and a test set; (3) pre-training the DNN layer by layer by using an auto-encoder, (4) determining a network structure, training a deep neural network model by using the training set, and optimizing and finely adjusting the model; and (5) carrying out early icing fault detectionon the fan blade by utilizing the trained model. According to the method, the influence of all data collected by the SCADA system on freezing of the fan blades is fully considered, fault detection ofearly freezing of the fan is achieved, and the detection accuracy reaches 98% or above.

Description

technical field [0001] The invention relates to a method for detecting an early icing fault of a fan blade based on a deep neural network, and belongs to the field of industrial system fault detection. Background technique [0002] Wind power is currently the most mature and promising renewable energy technology. The development of wind power in China has attracted worldwide attention. However, the particularity of wind energy acquisition determines that a large number of wind turbines need to be arranged in cold areas at high latitudes and high altitudes. However, the wind turbines working in cold areas are affected by the weather conditions such as frost, rain and wet snow, and it is very easy to cause the phenomenon of blade icing, which will lead to a series of consequences. This leads to the reduction of wind energy capture capacity, the loss of power generation, the breakage of wind turbine blades, and even safety accidents. [0003] Therefore, timely detection and ...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to propose a method for detecting early icing faults of fan blades based on a deep neural network, which is used to solve the problem of high cost in the traditional method of detecting icing by collecting the temperature of fan blades through sensors. A large number of detection variables collected by the SCADA system in the system are combined with the neural network to detect and diagnose the early icing of the fan blades, which can greatly reduce the cost of the wind farm while ensuring the accuracy

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