Evolutionary programming and multi-verse optimization based Technique for risk-based voltage stability control

Power system these days appears to work at high-stress load, which could trigger voltage security problems. This is due to the fact that the system will operate under low voltage conditions, which could be possibly below the allowable voltage limit. The voltage collapse phenomenon can become one of the remarkable issues in the power systems which can lead to severe consequences of voltage instability. This paper proposes a method for managing the voltage stability risk using two methods which are evolutionary programming (EP) and multiverse optimization (MVO). Consequently, EP and MVO were used to manage the risk in the power system due to load variations. The risk assessment is made in order to determine the risk of collapse for the system utilizing a pre-developed voltage stability index termed as Fast Voltage Stability Index (FVSI). It is used as the indicator of voltage stability conditions. Results obtained from the study revealed that the MVO technique is much more effective compared to EP.

Keywords:

Voltage Stability , Fast voltage stability index , Multiverse optimization (MVO) and Evolutionary programming (EP) , Risk assessment