Interval State Estimation Considering Randomness of Multiple Distributed Generations in Active Distribution Networks

DOI

10.2991/eee-19.2019.16How to use a DOI?

Keywords

Active distribution network, Interval state estimation, Distributed power, PSO

Abstract

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.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - Proceedings of the 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  - 10.2991/eee-19.2019.16
ID  - Yang2019/07
ER  -