State estimation for distribution power systems by applying an advanced optimization approach

New centralized automation functions in distribution system control centers are needed in order to ensure the control of both distribution network and connected DGs. Consequently, state estimators need to be developed for future distribution systems to assess the network's state in real time, i.e., minutes typical time frame, based on real, and virtual measurements. This paper proposes a new method based on the particle community optimization algorithm to estimate the state of electricity distribution networks. To increase the efficiency, accuracy, and speed of convergence rate to the answer and prevent the main particle assembly algorithm from fluctuating, the second particle assembly loop is provided in addition to the mutation algorithm and the isolation function. To consider the uncertainty of loads in distribution networks, modeled as active and reactive, virtual measurements with realistic error have been modeled. Results are presented and discussed to prove the efficiency of the proposed method, including sensitivity analysis for parameters used by the algorithm. The simulation results on two 6-bus and 34-bus radial distribution networks of the IEEE test show that the estimation of the distribution state based on the proposed DLM-PSO algorithm has a lower estimation error and standard deviation than the original HBMO, GA, WLS, and PSO algorithms.

Automated summary

This paper proposes a new method based on the particle community optimization algorithm for estimating the state of electricity distribution networks. By considering the uncertainty of loads and using virtual measurements, the estimation accuracy and efficiency are improved.