| Full text | |
| Author(s): |
Massignan, Julio A. D.
[1]
;
London, Jr., Joao B. A.
[1]
;
Bessani, Michel
[2]
;
Maciel, Carlos D.
[1]
;
Fannucchi, Rodrigo Z.
[3]
;
Miranda, Vladimiro
[4, 5]
Total Authors: 6
|
| Affiliation: | [1] Univ Sao Paulo, Dept Elect & Comp Engn, BR-13561010 Sao Carlos - Brazil
[2] Univ Fed Minas Gerais, Dept Elect Engn, BR-31270901 Belo Horizonte, MG - Brazil
[3] COPEL Distribuicao S, Dept Maintenance, BR-86050730 Londrina, Parana - Brazil
[4] Inst Engn Sistemas & Computadores Tecnol & Cienci, INESC TEC, P-4200465 Porto - Portugal
[5] Univ Porto, Fac Engn, P-4099002 Porto - Portugal
Total Affiliations: 5
|
| Document type: | Journal article |
| Source: | IEEE TRANSACTIONS ON SMART GRID; v. 13, n. 1, p. 526-540, JAN 2022. |
| Web of Science Citations: | 0 |
| Abstract | |
This paper presents a three-phase Distribution System State Estimator (DSSE) based on a Bayesian inference approach to manage different sampling rates of typical sources of information present in distribution networks. Such information comes from smart meters, supervisory control and data acquisition (SCADA) measurements, phasor measurement units and typical load profiles from pseudo measurements. The temporal aspect of the measurement set is incorporated in the estimation process by using a sampling layer concept, dealing separately with each group of measurements according to the respective updating rate. A Bayesian information fusion procedure provides the final estimation. The proposed DSSE consists in a multiple stage estimator that combines a prior model for the state variables, updated by new observations from measured values in each sampling layer, through Maximum a Posteriori estimation. This work also introduces an orthogonal method for the information fusion numerical solution, to tackle the severe ill-conditioning associated with practical distribution systems. Simulations with IEEE distribution test feeders and a Brazilian real distribution feeder illustrate the features of the proposed DSSE and its applicability. By exploring the concept of credibility intervals, the method is able to detect events on the grid, such as subtle load variation and contingencies, while maintaining accuracy. (AU) | |
| FAPESP's process: | 18/19150-6 - Resilience of complex systems with the use of dynamic Bayesian networks: a probabilistic approach |
| Grantee: | Carlos Dias Maciel |
| Support Opportunities: | Scholarships abroad - Research |
| FAPESP's process: | 18/00214-4 - Distribution systems state estimators: classical approaches and new algorithms for the Smart Grid |
| Grantee: | Julio Augusto Druzina Massignan |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate |
| FAPESP's process: | 14/50851-0 - INCT 2014: National Institute of Science and Technology for Cooperative Autonomous Systems Applied in Security and Environment |
| Grantee: | Marco Henrique Terra |
| Support Opportunities: | Research Projects - Thematic Grants |