Voltage Control In Smart Distribution Network With Limited Reactive Power Capacity Of Smart Inverter Using Machine Learning

Abstract

It is very important for consumers to have the operating voltages within the specified limit. Sometimes, this is not possible to provide by the grid operators although grid operators invest heavily in voltage regulating devices. With the integration of variable renewable energy systems in the grid, it has become more challenging for the grid companies to regulate them to provide stable voltages and good quality power. However, the control of inverters in renewable energy sources can be utilized for overcoming such challenges. Another aspect that should be considered is analyzing the capacity of the inverter to provide reactive power support to mitigate the voltage violation in the power system network. The amount of reactive power support from inverters is dependent on different loading conditions and the size of the inverter. Hence, the analysis of such scenarios is of the great significance of renewable energy sources and load, modeling of the network for implementing coordinated control from such inverters is another challenge. Hence, with available data from the grid and inverters, it is possible to model the system using machine learning (ML) algorithms. From the model developed using ML, reactive power support requirement from inverters, analysis of the impact of reactive power and active power from inverters and change of loading in the power system network can be analyzed. Reinforced machine learning is implemented in this work where the inverter learns to correct its action to adjust the voltages at the adjacent bus by controlling the flow of reactive power into the distribution network based on data available for the next stage. The voltages obtained on the far most bus were also within the specified limit even for the overloading condition. And the reactive power control was more robust and aggressive to keep the voltages within the specified limit as compared to the conventional Optimal Power Flow OPF tool.