Interval State Estimation Considering Randomness of Multiple Distributed Generations in Active Distribution Networks
Xiao-ping Yang, Yang Guo
Available Online July 2019.
- https://doi.org/10.2991/eee-19.2019.16How to use a DOI?
- Active distribution network, Interval state estimation, Distributed power, PSO
- The high-permeability Distributed Generation (DG) was connected to the power grid, so that the state estimation of the active distribution network(ADN) needs to consider the uncertainty of the DG output. In this paper, an interval state estimation method for active distribution network considering the randomness of Wind Turbine and PV output is proposed. The method uses the Extreme Learning Machine (ELM) to model the randomness of Wind Turbines and PV output in the form of interval numbers, and to perform ultra-short-term prediction on Wind Turbines and PV output interval, and use the output interval as pseudo measurement, based on the particle swarm optimization(PSO) State estimation of the ADN. The results of IEEE-33 system verification show that the state estimation results obtained by PSO algorithm are more accurate than the traditional weighted least square (WLS); the state estimation result presents an interval form, which can provide the dispatcher with a more intuitive system state quantity upper and lower bound information.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Xiao-ping Yang AU - Yang Guo PY - 2019/07 DA - 2019/07 TI - Interval State Estimation Considering Randomness of Multiple Distributed Generations in Active Distribution Networks BT - 2nd International Conference on Electrical and Electronic Engineering (EEE 2019) PB - Atlantis Press SP - 90 EP - 94 SN - 2352-5401 UR - https://doi.org/10.2991/eee-19.2019.16 DO - https://doi.org/10.2991/eee-19.2019.16 ID - Yang2019/07 ER -