A Modified Dynamic Bacterial Foraging Algorithm for an Enhanced Power System State Estimation

Power systems are getting more complex with the ongoing growing of the ever changing energy demand. This dynamic situation of the electric power networks makes the control and monitoring of the system a crucial issue. In order to have an accurate real time monitoring and representative models, state estimation practices are essential. This requirement becomes more significant for nonlinear systems such as the electric power networks. The objective of the state estimation problem is to apply a variety of statistical and optimization methods in order to determine the best estimate of the power system variables. The variables of the power system are conventionally measured using various common metering devices in spite of the complexity and gradual expansion of the networks. However, these measuring meters are associated with errors and inaccurate output readings due to several operational, communicational and device-linked causes. Consequently, determining an improved and optimized estimation of the system state is significant and essentially needed, and hence this topic is getting more attraction among the researchers. The most typically applied approach to deal with the state estimation problem is the Weighted Least-squares (WLS) method. In this paper a hybrid algorithm is introduced utilizing a WLS-based dynamic bacterial foraging algorithm (DBFA). The proposed algorithm was applied and validated using the well-known IEEE 14-bus system. The results demonstrated the effectiveness and superiority of the algorithm when compared to some of other techniques used to tackle the state estimation issue