Real-time computing of power flows and node voltages in electrical energy network using decision trees

Abstract

In sustainable operation of electrical energy network, it is necessary to compute in real-time power flows and voltages at nodes for prioritizing power injection from clean energy resources. Intermittent renewable energy sources are likely to create voltage and power balancing issues and to maintain the voltage security of electrical network, real-time information of network power flows and bus voltages are required accurately and instantaneously. This paper presents an approach based on decision trees (DT) for real-time estimation of power flows within the electrical energy network and node voltages. A single tree structure is built for estimation of discrete (or categorical) as well as continuous values of line flows and node voltages of each line and node separately. A simple binary decision tree (BDT) and regression tree (RT) are used for estimation of discrete values and continuous values respectively. The training and testing patterns are generated by performing power flow analysis on an electrical energy network. Once the DT is trained, it estimates the line power flows and bus voltages with desired accuracy. The accuracy of the DT model is tested on a typical IEEE 30-bus system, using test patterns. Result shows that mean absolute error in case of line flow estimation for line number 1 and 10 are found to be 0.0028 p. u. and 0.0017 p. u. Also mean absolute error in case of bus voltage estimation for bus number 3 and 10 are found to be 0.0019 p. u. and 0.0016 p. u. Above results are suggestive of instantaneous estimationwith desired accuracy of line flow and bus voltages, which is the need of the hour for sustainable electrical energy network with integration of cleaner energy resources.Since, DT gives instantaneous result therefore suitable for real-time applications in sustainable electrical energy management system.