Description

Wind energy integration research generally relies on complex sensors located at remote sites. The procedure for generating high-level synthetic information from databases containing large amounts of low-level data must therefore account for possible sensor failures and imperfect input data. This Application presents an empirical methodology that can efficiently preprocess and filter the raw wind data using only aggregated active power output and the corresponding wind speed values at the wind farm. Raw wind data properties are analyzed, and all the data are divided into six categories according to their attribute magnitudes from a statistical perspective. Four data-mining approaches for wind turbine power curve monitoring are compared. Power curve monitoring can be applied to evaluate the turbine power output and detect deviations, causing financial loss. In this research, cluster center fuzzy logic, neural network, and -nearest neighbor models are built and their performance compared against literature. Recently developed adaptive neuro-fuzzy-interference system models are set up and their performance compared with the other models, using the same data. Literature models often neglect the influence of the ambient temperature and the wind direction. To use unsupervised algorithms, we adopted an unsupervised l earning approach based on the local outlier factor (LOF)-identifying algorithm introduced. The LOF of every data point is computed using a novel concept of the degree of similarity among the individual data points, and hence invalid data are detected as abnormal outlier factors.