AI Controller-Based Integration of Photovoltaic and Doubly-Fed Wind Power Hybrid Grid

As renewable energy increasingly penetrates power systems, the efficient integration of photovoltaic (PV) generation and wind power has become a critical challenge for grid stability. This paper explores how AI controllers enable coordinated optimization of hybrid grids combining PV and doubly-fed induction generator (DFIG) based wind power, covering the complete technical chain from device-level modeling to system-level control.

The complexity of hybrid grids stems from the distinct dynamic characteristics of PV and DFIG units: PV output power experiences rapid fluctuations due to instantaneous changes in solar irradiance, while DFIG turbines achieve active/reactive power decoupling control through rotor-side converters, yet their shaft mechanical inertia significantly impacts grid frequency stability. Traditional PI controllers struggle to coordinate their response differences under varying weather scenarios.

Breakthrough capabilities of AI controllers include: Adaptive parameter tuning through deep reinforcement learning dynamically adjusts control parameters to balance the fast response of PV inverters with the inertial support from DFIG turbines during voltage dips or frequency deviations. Multi-timescale prediction using LSTM neural networks enables AI controllers to forecast PV output drops and wind speed trends in advance, facilitating pre-scheduled charging/discharging strategies for energy storage systems. Fault ride-through optimization allows AI algorithms to compute optimal crowbar activation timing and STATCOM compensation levels in real-time during symmetrical/asymmetrical grid faults, preventing DFIG disconnection due to overvoltage.

Key dimensions for simulation modeling comprise: Device-level modeling requiring accurate simulation of DFIG's three-mass shaft torsional vibration model and dynamic MPPT characteristics of PV arrays. Control loop validation in platforms like PSCAD/EMTP or RT-LAB necessitates injecting measured irradiance/wind speed data streams to test the generalization capability of AI controllers. Grid interaction analysis through eigenvalue analysis reveals the impact of AI controllers on small-signal stability of hybrid grids, with particular focus on subsynchronous oscillation risks.

Future research directions involve deploying AI controllers via edge computing to reduce communication latency, and leveraging digital twin technology for online control strategy updates. This intelligent hybrid grid architecture will establish a new technical paradigm for high-penetration renewable energy integration.