PMID- 31080312
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20200930
IS  - 0885-8950 (Print)
IS  - 1558-0679 (Electronic)
IS  - 0885-8950 (Linking)
VI  - 33
DP  - 2018
TI  - MMSE-based analytical estimator for uncertain power system with limited number of 
      measurements.
LID - 10.1109/TPWRS.2018.2801121 [doi]
AB  - The expected penetration of a large number of renewable distributed energy 
      resources (DER's) is driving next generation power systems toward uncertainties 
      that can have a huge impact on the reliability and complexities of state 
      estimation. Therefore, the stochastic power flow (SPF) and forecasting-aided 
      state estimation of power systems integrating DER's are becoming a major 
      challenge for operation of the future grid. In this paper we propose a new state 
      estimation method referred to as 'mean squared estimator' (MSE) to deal with the 
      uncertain nature of the power system parameters. The estimator benefits from the 
      prior study of SPF, which involves the probability density functions (PDF's) of 
      the system parameters. The main advantage of this estimator is based on its 
      ability to instantaneously incorporate the dynamics of the power system. 
      Moreover, the analytical formula of MSE expresses the mean value of the estimated 
      parameters corrected by an additional term that takes into account the 
      measurement of the parameters. It is shown that the proposed MSE can provide an 
      accurate state estimation with a limited number of measurements with guaranteed 
      convergence. MSE has been tested using IEEE 14, 30, 39 and 118 bus models for 
      different measurement redundancies. The results have been compared to methods 
      such as weighted least square (WLS), unscented Kalman filter (UKF) and 
      compressive sensing-based UKF (CS-UKF). The numerical results show superior 
      performances, especially under a limited number of measurements where WLS and UKF 
      may lead to divergence.
FAU - Bilil, Hasnae
AU  - Bilil H
AD  - Advanced Network Technologies Division, National Institute of Standards and 
      Technology, MD, USA.
FAU - Gharavi, Hamid
AU  - Gharavi H
AD  - Advanced Network Technologies Division, National Institute of Standards and 
      Technology, MD, USA.
LA  - eng
GR  - 9999-NIST/Intramural NIST DOC/United States
PT  - Journal Article
PL  - United States
TA  - IEEE Trans Power Syst
JT  - IEEE transactions on power systems : a publication of the Power Engineering 
      Society
JID - 101212324
PMC - PMC6508601
MID - NIHMS971657
OTO - NOTNLM
OT  - Dynamic state estimation
OT  - Gaussian mixture model
OT  - analytic estimator
OT  - limited number of measurements
OT  - minimum mean-squared error
EDAT- 2018/01/01 00:00
MHDA- 2018/01/01 00:01
PMCR- 2019/05/09
CRDT- 2019/05/14 06:00
PHST- 2019/05/14 06:00 [entrez]
PHST- 2018/01/01 00:00 [pubmed]
PHST- 2018/01/01 00:01 [medline]
PHST- 2019/05/09 00:00 [pmc-release]
AID - 10.1109/TPWRS.2018.2801121 [doi]
PST - ppublish
SO  - IEEE Trans Power Syst. 2018;33:10.1109/TPWRS.2018.2801121. doi: 
      10.1109/TPWRS.2018.2801121.