A method for cleaning wind power anomaly data by combining image processing with community detection algorithms

Current methodologies for cleaning wind power anomaly data exhibit limited capabilities in identifying abnormal data within extensive datasets and struggle to accommodate the considerable variability and intricacy of wind farm data.Consequently,a method for cleaning wind power anomaly data by combining image processing with community detection algorithms(CWPAD-IPCDA)is proposed.To precisely identify and initially clean anomalous data,wind power curve(WPC)images are converted into graph structures,which employ the Louvain community recognition algorithm and graph-theoretic methods for community detection and segmentation.Furthermore,the mathematical morphology operation(MMO)determines the main part of the initially cleaned wind power curve images and maps them back to the normal wind power points to complete the final cleaning.The CWPAD-IPCDA method was applied to clean datasets from 25 wind turbines(WTs)in two wind farms in northwest China to validate its feasibility.A comparison was conducted using density-based spatial clustering of applications with noise(DBSCAN)algorithm,an improved isolation forest algorithm,and an image-based(IB)algorithm.The experimental results demonstrate that the CWPAD-IPCDA method surpasses the other three algorithms,achieving an approximately 7.23%higher average data cleaning rate.The mean value of the sum of the squared errors(SSE)of the dataset after cleaning is approximately 6.887 lower than that of the other algorithms.Moreover,the mean of overall accuracy,as measured by the F1-score,exceeds that of the other methods by approximately 10.49%;this indicates that the CWPAD-IPCDA method is more conducive to improving the accuracy and reliability of wind power curve modeling and wind farm power forecasting.

An Algorithm for Practical Power Curve Estimation of Wind Turbines

Practical power curve estimation is necessary for evaluating the actual power output of a wind farm;since a power curve provided by the wind turbine manufacture will be different with the actual power curve following several years of operation.It can be estimated using the collected power output data including wind power generation and wind speed.This data is commonly ill-distributed due to a noticeable number of outliers,which impose a serious bias to the estimation models obtained from this data.It introduces an interesting challenge in estimation of a power curve.In this paper,an intelligent algorithm is proposed for estimating a power curve using the measured data while modeling and bias errors,imposed to the estimation model by the outliners,are minimized.More specifically,this algorithm is designed based on the Statistical Analysis Software(SAS)programming software package in order to facilitate analyzing and managing big datasets of wind speed and wind power generation.The effectiveness and practical application of the proposed algorithm is demonstrated using a real-world dataset.