Soft Computing Techniques in Wind Conversion Systems

Abstract

In recent years, there has been a lot of interest in using soft computing approaches in designing and optimizing green energy sources. Soft computing refers to a class of computational methods inspired by human intelligence and demonstrated to be useful in dealing with ambiguous problems. This review chapter describes the implementation of soft computing techniques in creating novel, low-cost wind transformation technologies. The application of artificial neural networks (ANNs) in wind turbine control systems is investigated in this review. ANNs have proved their ability to improve wind turbine efficiency through problem detection and condition monitoring. ANNs adaptively alter the operation of the generator by analyzing real-time data from numerous sensors, enhancing energy extraction, and limiting mechanical strain. Fuzzy logic controllers provide a flexible framework for dealing with imprecise and uncertain data, enabling robust control in the face of changing wind speed, direction inputs, etc. The genetic algorithms (GAs) addressed here use evolved principles to seek ideal blade forms while considering objectives like power production, structural integrity, and noise reduction. GAs can identify blade designs that outperform traditional techniques through rounds of genetic variation and screening. Support vector machines (SVMs) and adaptive neuro-fuzzy inference systems (ANFIS) prove highly effective in precisely forecasting wind speeds and directions at specific locations. This accuracy is achieved through the integration of data from diverse sources, including weather stations, satellite imagery, and numerical weather prediction models. These predictions described in this analysis aid in wind farm site selection and in optimizing turbine configuration. PSO algorithms (particle swarm optimization) can maximize energy extraction from available wind resources, resulting in increased power output and reducing the cost of the overall windmill. This review chapter explores the application, challenges, and future of smart cloud computing in practical wind generation techniques in the circular economy.