Dynamic Voltage Stability Analysis Using Decision Trees

Abstract

All over the world, power systems are being operated in more stressed conditions as modernisation and globalization increase demand for electricity. In Kenya, the government has in the last few years been on a drive to connect more consumers to the national grid in line with its Vision 2030. The national electricity distribution utility, Kenya Power Company, has been on a drive to connect 300,000 new customers each year to the grid since 2009 while the Rural Electrification Authority was established in 2007 and is mandated with connecting rural customers. The increase in the consumer load has however not been matched by increase in the generation. Consequently, the system is operated closer to its stability limit and is thus more prone to instability in case of contingencies as was witnessed during the April - June 2012 long rains when the system experienced nationwide blackouts. It further puts pressure on system controllers to operate the system within the lower security margins and defensively operate the system during conditions of peak loads. This research thesis aimed at evaluating the dynamic voltage security of the Kenya Power System using decision trees. These would establish the real time voltage security status of the system and the likely final voltage stability status if the system is allowed to continue operating with the given loading – contingency configuration. This would allow system operators to quickly establish the voltage stability status of the system. At the same time, it will help in indicating how close the voltage insecure buses are to voltage collapse by using an Artificial Neural Network (ANN) based proximity-to-collapse index instead of the conventional Continuation Power Flow (CPF) which takes a lot of computing effort. Dynamic Voltage stability analysis of any system is studied by considering load changes within the system and how voltage magnitudes at the load buses within the system are affected by the load changes. The analysis can be further complicated by considering probable contingencies within the system that are caused by line outages. The dynamic nature of the load can be considered by using dynamic load models and evaluating the changes, with time, of the bus voltages. However, the model-driven voltages take time to compute which may not give the system operator time to act since voltage collapse occurs within a very short period. This research considered 100 random load variations without regard to power factor for each probable single – line outage. This gave many snapshots of the dynamic load from which the dynamic tendency of the system was then evaluated by constructing a decision tree for each load bus within the system. The algorithm was first validated on the IEEE test systems (9 - Bus and 30 - Bus ) before being applied to the Kenya Power System. The Decision Trees show the relationship between a particular bus’s voltage magnitude and the contingency and power demand at other buses. The results demonstrated relationships between bus power demands and voltages at other buses that would otherwise not be visible by simply evaluating load flow studies from static snapshots of the Kenyan system. The resulting decision trees for the buses within the Kenya Power System show the most influential variable at each bus and give a binary split for each variable, and the expected voltage magnitude at that bus.