Artificial-Intelligence-Enabled Wind-Grid Intelligence Systems: A Systematic Review of AI Evolution, Functional Intelligence Layers, and Deployment Readiness

The accelerated adoption of renewable energy (RE) systems has heightened the complexity of electricity grid operations, primarily due to the uncertainty, variability, and distributed nature of wind power generation. Artificial intelligence (AI) has become a transformative tool for addressing these challenges by facilitating advanced forecasting, adaptive control, optimization, and intelligent decision-making. Nevertheless, the maturity of AI-enabled Wind-Grid Intelligence Systems (WGIS), encompassing functional capabilities, methodological development, and practical deployment readiness, remains inadequately characterized. This systematic review assesses the current landscape of AI-enabled WGIS by synthesizing findings from 56 empirical studies. The literature was categorized using a structured framework that examined each study’s evidence source, AI methodologies, WGIS layer, target application, and validation maturity. The analysis indicates that WGIS research encompasses both wind-specific applications and transferable intelligent-grid technologies. Specifically, 48.2% of studies focus on wind-related challenges, while 51.8% address broader intelligent-grid solutions. Methodological trends reveal a shift toward advanced AI architectures, with deep learning as the predominant approach (50.0%), followed by reinforcement learning (33.9%) and traditional machine learning (8.9%). Although techniques such as graph intelligence and physics-informed AI are currently underrepresented, they offer considerable potential to enhance the reliability and contextual awareness of WGIS architectures. Analysis using a five-layer WGIS intelligence framework demonstrates that layer 2, predictive intelligence, is the primary research focus, accounting for 51.8% of studies, followed by layer 4, adaptive control intelligence, at 21.4%. In contrast, layer 5, strategic intelligence, is underexplored, with only 7.1% of studies addressing this area. This distribution indicates that current research prioritizes prediction and operational support rather than autonomous strategic decision-making. Furthermore, the validation maturity assessment reveals a substantial gap between AI development and practical implementation. Of the studies reviewed, 60.7% were classified at V1 using offline/stored-dataset validation, 37.5% at V2 using software simulation or co-simulation, and a single study (1.8%) reached V3 dedicated real-time hardware-coupled laboratory validation. No study satisfied the predefined criteria for pilot-scale demonstration or full operational deployment. Methodological-quality appraisal classified 39 studies (69.6%) as Higher-confidence and 17 (30.4%) as Moderate-confidence, with no Lower-confidence studies; restriction to the Higher-confidence subset did not alter the principal rank order conclusions. Accordingly, 98.2% of the evidence remained within the V1–V2 stages, while no study reached pilot-scale (V4) or operational (V5) deployment, underscoring the need for more robust validation pathways and industry-scale demonstrations. This review proposes a future WGIS development roadmap focused on integrated AI architectures, trustworthy AI, digital twin technologies, physics-informed learning, and real-world validation frameworks. The findings indicate that progress in WGIS will require a shift from prediction-oriented AI models to adaptive, explainable, and operational intelligent energy infrastructures that can support resilient renewable energy integration.