Fault early warning method of wind generating set

Abstract

The invention provides a fault early warning method of a wind generating set. The fault early warning method comprises the following steps of: 10) collecting the actual temperature value R and temperature related parameter data of a monitored point for N times in a sampling period; 20) inputting the collected actual temperature value R and the temperature related parameter data of the monitored point into a predetermined temperature mould, computing to obtain a temperature predicted value of the monitored point and taking the temperature predicted value as the theoretical temperature value P of the monitored point; 30) obtaining a temperature deviation DEV, a temperature mean-square deviation MSE and related coefficients SCC of the theoretical temperature value P and the actual temperature value; and 40) estimating the health condition of the wind generating set on the basis of the temperature deviation DEV, the temperature mean-square deviation MSE and related coefficients SCC. By means of the fault early warning method, the accuracy of estimating the health condition of the wind generating set can be improved.

Description

technical field [0001] The invention relates to wind power generation technology, in particular to a fault early warning method suitable for wind power generating sets. Background technique [0002] With the consumption of non-renewable resources such as oil and coal and the improvement of environmental awareness, people have paid more and more attention to the application of renewable energy. Among them, wind energy, as a clean renewable energy, is playing an increasingly important role in global energy conservation and emission reduction. Correspondingly, wind power generators that can convert wind energy into electrical energy have also received more and more attention and development. [0003] Usually, wind power generators are installed in the field, and their working environment is often relatively harsh, so they are prone to failure. In order to be able to detect the faults of the wind turbines in time to prevent problems such as reduced power generation efficiency a...

AI Technical Summary

Problems solved by technology

[0006] First, since only one temperature threshold is set corresponding to each monitored point, therefore, figure 1 The method shown can only judge whether the operating state of the wind turbine is normal or abnormal based on whether the current temperature of the monitored point exceeds the temperature threshold, that is, figure 1 The method shown can only divide the operating status of wind turbines into normal and abnormal, but cannot make a finer division, so that figure 1 The method shown has low estimation precision

In this case, when the operating state of the wind turbine is close to abnormal state, through figure 1 The method shown still judges that the current operating state of the wind turbine is normal, so that the failure of the wind turbine cannot be detected in time, so that the wind turbine that is approaching the abnormal state works at high temperature for a long time, resulting in shortened service life and low power generation efficiency. Lowering and other issues

In addition, due to the figure 1 The method shown cannot evaluate whether the wind turbine is far away from the normal operation state or slightly deviated from the normal state, so if the wind turbine is slightly deviated from the normal state but still maintains normal operation, the alarm will still be issued, so that the operator or the automatic Based on the alarm, the control system shuts down for maintenance, resulting in a waste of manpower and material costs, and seriously affecting the power generation efficiency of the wind turbine

[0007] Second, since only one temperature threshold is set corresponding to each monitored point, when the temperature threshold itself is set in a wrong way and / or the collected temperature data is wrong, the collected temperature and the temperature threshold can still be relied on. Judging the operating status of wind turbines based on the comparison results between them will often lead to misjudgment, resulting in false positives or false negatives, and thus affect the follow-up operational measures for wind turbines. For example, when the wind turbines were originally running When a false alarm occurs normally, the operator or the automatic control system will shut down the wind turbine for maintenance based on the alarm, which will cause a waste of manpower and material costs and seriously affect the power generation efficiency of the wind turbine

Moreover, in the case of underreporting, the wind turbine will work at abnormal temperature for a long time and eventually break down. In this way, it will not only shorten the service life of the fan, but also seriously affect the performance of the wind turbine due to downtime maintenance. power generation efficiency

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