Appraising machine learning classifiers for discriminating rotor condition in 50W–12V operational wind turbine for maximizing wind energy production through feature extraction and selection process

Abstract

Wind energy is one of nature’s most valuable green energy assets, as well as one of the most reliable renewable energy supplies. Wind turbine blades convert wind energy into electric energy. Wind turbine blades range in size from 25 to 120 m, depending on the demands and efficiency necessary. Owing to ambient influences and wide structures, the blades are subject to various friction forces that might harm the blades. As a result, the generation of power and the shutdown of turbines are both affected. Downtimes are reduced when blades are detected on a regular basis, according to structural health management. On the 50-W, 12-V wind turbine, this research investigates the use of vibration signals to anticipate deterioration. The machine learning (ML) method establishes a nonlinear relationship between selected important damage features and the related uniqueness measures. The learning algorithm was trained and tested based on the excellent state of the edge. To forecast blade faults, classifier models, such as naive Bayes (NB), multilayer perceptron (MLP), linear support vector machine (linear_SVM), one-deep convolutional neural network (1DCNN), bagging, random forest (RF), XGBoosts, and decision tree J48 (DT) were used, and the results were compared according to their parameters to propose a better fault diagnostics model.

Power quality enhancement for Thailand's wind farm using 5 MWh Li-ion battery energy storage system

“…Fluctuations in energy production create imbalances in the power distribution line, leading to issues such as overvoltage or undervoltage, which result in feeder trips or necessitate shutting down the wind power plants [ 6 , 7 ]. Due to the absence of an energy storage system, high-penetration wind power plants are disconnected from the distribution system to maintain grid stability and the Distribution System (DS) unless power quality issues such as harmonic distortion, voltage, and frequency variations arise [ [8] , [9] , [10] , [11] ]. In this study, a detailed review was conducted to understand the necessity of energy storage for wind power plants.…”