Analysis of Wind Characteristics for Grid-Tied Wind Turbine Generator Using Incremental Generative Adversarial Network Model

Abstract

Wind attribute analysis is a crucial aspect of meteorological and environmental research, with applications ranging from renewable energy generation to weather forecasting. However, existing models encounter several challenges in accurately and comprehensively characterizing wind positions. In this context, the proposed Incremental Tuned Generative Adversarial Network model (incremental GAN model), based on an unsupervised learning approach, introduces innovative solutions to overcome these challenges and enhance the precision and reliability of wind position analysis. This research aims to enhance the reliability and efficiency of wind energy generation by analyzing wind conditions and providing accurate data for decision-making. It introduces an Incremental GAN that refines parameters based on various factors. This GAN model learns and predicts these parameters over time, improving its performance. It incorporates advanced techniques like a 2-level fused discriminator and self-attention for precise predictions of wind characteristics. The GAN model generates important parameters such as droop gain, which influences generator output in response to load or generation changes, aiding grid stability. It also optimizes the frequency control of different types of generators in the presence of wind farms. The model continuously monitors wind farm conditions, adjusting power injection into the grid as needed for efficient and reliable wind energy utilization.

Keywords

• Renewable energy system (RES)
• wind energy integration (WEI)
• power system stability (PSS)
• unsupervised learning (USL)
• incremental tuned generative adversarial network (IGAN)
• doubly fed induction generator (DFIG)