Image-Based Abnormal Data Detection and Cleaning Algorithm via Wind Power Curve

TL;DR

This paper introduces an image-based algorithm for detecting and cleaning abnormal wind turbine data using wind power curve images, effectively categorizing and marking anomalies to improve data quality.

Contribution

The paper presents a novel image-based approach utilizing wind power curve images and mathematical morphology to detect and clean abnormal wind turbine data, outperforming traditional methods.

Findings

Effective detection of three types of abnormal data.

High accuracy demonstrated across 33 turbines from two farms.

Outperforms existing algorithms in effectiveness and efficiency.

Abstract

This paper proposes an image-based algorithm for detecting and cleaning the wind turbine abnormal data based on wind power curve (WPC) images. The abnormal data are categorized into three types, negative points, scattered points, and stacked points. The proposed algorithm includes three steps, data pre-cleaning, normal data extraction, and data marking. The negative abnormal points, whose wind speed is greater than cut-in speed and power is below zero, are first filtered in the data pre-cleaning step. The scatter figure of the rest wind power data forms the WPC image and corresponding binary image. In the normal data extraction step, the principle part of the WPC binary image, representing the normal data, is extracted by the mathematical morphology operation (MMO). The optimal parameter setting of MMO is determined by minimizing the dissimilarity between the extracted principle part and the reference WPC image based on Hu moments. In the data mark step, the pixel points of scattered and stacked abnormal data are successively identified. The mapping relationship between the wind power points and image pixel points is built to mark the wind turbine normal and abnormal data. The proposed image-based algorithm is compared with k-means, local outlier factor, combined algorithm based on change point grouping algorithm and quartile algorithm (CA). Numerous experiments based on 33 wind turbines from two wind farms are conducted to validate the effectiveness, efficiency, and universality of the proposed method.