Due to increasing penetration of solar power generation, distribution grids are facing a number of challenges. Frequent reverse active power flows can result in rapid fluctuations in voltage magnitudes. However, with the revised IEEE 1547 standard, smart inverters can actively control their reactive power injection to minimize voltage deviations and power losses in the grid.

Reactive power control and globally optimal inverter coordination in real-time is computationally and communication-wise demanding, whereas the local Volt-VAR or Watt-VAR control rules are subpar for enhanced grid services. This thesis uses machine learning tools and poses reactive power control as a kernel-based regression task to learn policies and evaluate the reactive power injections in real-time. This novel approach performs inverter coordination through non-linear control policies centrally designed by the operator on a slower timescale using anticipated scenarios for load and generation. In real-time, the inverters feed locally and/or globally collected grid data to the customized control rules. The developed models are highly adjustable to the available computation and communication resources. The developed control scheme is tested on the IEEE 123-bus system and is seen to efficiently minimize losses and regulate voltage within the permissible limits.