Low-Latency Control of Wind Energy Systems Using Edge-Deployed Deep Learning and Multi-Objective Genetic Algorithm Optimization for Real-Time Harmonic Forecasting in Industrial Grids
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The growing contribution of wind turbines to industrial grids is causing growing concern over power quality, especially the appearance of harmonic distortion affecting stability, efficiency and equipment lifetime. Classical harmonic compensation techniques are based on central processing; hence they introduce delay which make them unable to cope with quickly varying wind. A new efficient, low latency control architecture for wind energy systems is presented in this paper where edge-based deep learning models and MOGA configurations provide the ability for on-line harmonic prediction and optimal control actions. Two set of lightweight neural networks processing units are integrated at field level near wind turbine controllers to predict harmonic components milliseconds in advance, which can mitigate the burden on cloud or centralized processing. These predictions are used as input to a MOGA-driven optimization engine which minimizes distortion concurrently with reactive power support and voltage stability. Well-detailed simulations are carried out using industrial standard wind profiles and detailed grid models from a real situation under different disturbance and load situations. Experiments show that the model deployed at edge can reduce latency remarkably (up to 65% if compared with cloud-based forecasting) without scarifying much predictive accuracy. The MOGA-based controller minimizes the overall THD by 30–45% and improves dynamic grid stability without adding extra computational work. The proposed edge-intelligent and multi-objective optimized control strategy is a scalable solution for future intelligent industry grids with high penetration of renewable energy. This study provides a basis for decentralized, predictive, real-time harmonic-aware control methodologies that can be generalized to other DER.
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