Fan power curve data preprocessing method based on outlier detection

Abstract

The invention discloses a fan power curve data preprocessing method based on outlier detection. According to the method, based on collected fan data comprising wind speed, active power, environment temperature, a pitch angle, etc. and real-time operating data obtained through a supervisory control (SCADA) system, outlier detection criteria are used to perform abnormal data cleaning, air density obtained through calculation is utilized to correct wind speed data, an outlier detection algorithm is used to detect and eliminate outliers in fan power curve data. According to the data preprocessingmethod, the outlier detection criteria guarantee the effectiveness of fan data information; wind speed correction guarantees the comparability of the fan data; and the outlier detection algorithm guarantees the reliability of final power curve data. The method is based on data driving, does not have a special requirement on data and is high in universality. Compared with the prior art, data credibility is higher, the process is more standard, and a high theoretical property and practicability are achieved.

Description

technical field [0001] The invention relates to a fan power curve data preprocessing method, in particular to a fan power curve data preprocessing method based on abnormal point and outlier point detection. Background technique [0002] In a modern society where traditional fossil energy resources are scarce and seriously polluted, wind energy is widely favored by the public as a pollution-free and renewable new energy source, and the wind power industry has thus become one of the new renewable energy industries that are vigorously developed at home and abroad. . In my country, the construction of wind farms and related research work have significantly improved both in terms of quantity and quality in the past ten years. a series of negative factors. During the use of wind turbines today, due to the intermittent and highly uncertain characteristics of wind speed, it has a great impact on the performance evaluation of wind turbines themselves, and the correct performance and...

AI Technical Summary

Problems solved by technology

[0004] (1) The traditional power curve acquisition method has not formed a data cleaning specification process for the fan operation data;

[0005] (2) All data under different working conditions were directly analyzed during the research process, resulting in inaccurate subsequent data analysis results;

[0006] (3) Only rely on the feedback of the fault detection system to judge the fault data, and the influence of potential faults cannot be ruled out

[0007] Therefore, the existing power curve acquisition technology cannot effectively obtain reliable information of the fan power curve from the data set in terms of data preprocessing, and corresponding process standardization improvements are required

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