Diagnostic method and system for abnormal shift of wind turbine generator power curve

Abstract

The invention discloses a diagnostic method for abnormal shift of a wind turbine generator power curve. The diagnostic method comprises the following steps: collecting unit power data under different wind speeds; sequencing the collected data according to wind speed, calculating the power shift area under the unit accuracy; setting up a diagnostic model, confirming the diagnostic parameter, and determining positive and negative warning lines and a normal zone according to the diagnostic parameter; diagnosing and comparing the calculation result with the positive and negative warning lines, wherein for more positive warning line, it is diagnosed that the wind speed measurement is wrong; for more negative warning line, it is diagnosed that the blade aerodynamic performance is abnormal. According to the diagnostic method and the diagnostic device, the data differential integral principle is introduced to a wind power plant or a cluster monitoring early warning system of a large-scale wind power plant and judge 'cross-border' degree of the power curve; the wind power matching and stability problems of the wind turbine generator, the wind speed measurement, the wind turbine generator control problem, the blade freezing of the wind turbine generator, abnormal front wind and crosswind of a wind indicator, and other power output problems can be warned according to the power curve shifting diagnosis.

Description

technical field [0001] The invention relates to the technical field of wind power, in particular to a method and system for diagnosing abnormal drift of the power curve of a wind turbine. Background technique [0002] When the wind turbine power curve is abnormal, it is often hidden. In the remote monitoring system, unless professional analysts analyze the power curve of the wind turbine or experienced personnel monitor the wind farm, the conventional power curve is automatically identified and compared. It is difficult to find the drift problem of the output power of the wind turbine in time with the method. In the process of technical transformation and technical debugging, the initial parameter setting error of the wind turbine, the drift of the wind measuring instrument, the freezing of the blades, the pollution and other situations can cause the power curve of the wind turbine to distort or drift, and directly affect the power generation and output of the wind turbine. ...

AI Technical Summary

Problems solved by technology

[0002] When the wind turbine power curve is abnormal, it is often hidden. In the remote monitoring system, unless professional analysts analyze the power curve of the wind turbine or experienced personnel monitor the wind farm, the conventional power curve is automatically identified and compared. It is difficult to detect the drift problem of the output power of the wind turbine in time with the method

In the process of technical transformation and technical debugging, the initial parameter setting error of the wind turbine, the drift of the wind measuring instrument, the freezing of the blades, the pollution and other situations can cause the power curve of the wind turbine to distort or drift, and directly affect the power generation and output of the wind turbine. power

[0003] The existing power curve abnormality judgment is realized by calculating the degree of compliance of the power curve. The guaranteed power curve is compared with the actual power curve obtained by converting the air density into the standard power curve. This degree of compliance algorithm is based on the wind speed point. The calculation does not consider the weight, so there is a flaw that is not rigorous enough, and the average of the coincidence degree of each wind speed point of the curve after the calculation of the entire wind speed section is not able to reflect the problem of the power curve

In addition, although this algorithm has a certain effect on the average power curve, it is inefficient and needs to collect more basic information such as geographical environment. Therefore, there are many factors to be considered and there are certain errors. Although it can be used as an indicator for wind farm evaluation Benchmarking activities, but when the power curve drifts left and right, its average value can easily cause judgment errors. Therefore, the above-mentioned index comparison algorithm has certain loopholes, and it only belongs to the index evaluation method, and it is given in the wind farm. Specific optimization scheme for diagnosis and alarm of wind turbine power curve abnormality

[0004] It can be seen that the above-mentioned existing wind turbine power curve abnormality diagnosis method obviously has inconvenience and defects in method and use, and needs to be further improved urgently.

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