Voltage Regulation of Smart Grids using Machine Learning Tools
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Smart inverters have been considered the primary fast solution for voltage regulation in power distribution systems. Optimizing the coordination between inverters can be computationally challenging. Reactive power control using fixed local rules have been shown to be subpar. Here, nonlinear inverter control rules are proposed by leveraging machine learning tools. The designed control rules can be expressed by a set of coefficients. These control rules can be nonlinear functions of both remote and local inputs. The proposed control rules are designed to jointly minimize the voltage deviation across buses. By using the support vector machines, control rules with sparse representations are obtained which decrease the communication between the operator and the inverters. The designed control rules are tested under different grid conditions and compared with other reactive power control schemes. The results show promising performance.
General Audience Abstract
With advent of renewable energies into the power systems, innovative and automatic monitoring and control techniques are required. More specifically, voltage regulation for distribution grids with solar generation is a can be a challenging task. Moreover, due to frequency and intensity of the voltage changes, traditional utility-owned voltage regulation equipment are not useful in long term. On the other hand, smart inverters installed with solar panels can be used for regulating the voltage. Smart inverters can be programmed to inject or absorb reactive power which directly influences the voltage. Utility can monitor, control and sync the inverters across the grid to maintain the voltage within the desired limits. Machine learning and optimization techniques can be applied for automation of voltage regulation in smart grids using the smart inverters installed with solar panels. In this work, voltage regulation is addressed by reactive power control.
- Masters Theses