Scalable cellular computational network based WLS state estimator for power systems

Modern interconnected electric power systems are made up of a large number of buses to meet the demand of electricity across large geographical distances. The large number of buses and interconnections across multiple areas result in a complex network and cause an increase in computational requirements on the processor. In order to meet the requirements of this increased complexity for state estimation, distributed estimation is getting attention nowadays. A new approach based on Cellular Computational Network (CCN) for static state estimation is proposed to overcome the computational demand of large power networks in general. The CCN architecture requires a cell at every bus where the states need to be estimated. A cell uses locally available information to estimate voltage magnitude and angle of its bus. The cells exploit output information of other cells in some electrical proximity prior to computing the outputs for next time step. Beside the promise of scalability of the CCN architecture, a fully observable system for state estimation and other applications can be realized. As the traditional estimators take all the measurements at a time and executes the estimation, missing some of the measurements may cause it to loose observability. In this paper, CCN based architecture is implemented with the popular Weighted Least Square (WLS) estimator on nonlinear power flow equations to estimate off-line data. Through simulation, the scalability and observability of the CCN based framework is investigated.