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Three-phase Multiarea state estimator for large scale distribution systems

Grant number: 16/19646-6
Support type:Scholarships in Brazil - Doctorate
Effective date (Start): May 01, 2017
Effective date (End): July 31, 2021
Field of knowledge:Engineering - Electrical Engineering - Power Systems
Cooperation agreement: Coordination of Improvement of Higher Education Personnel (CAPES)
Principal researcher:Joao Bosco Augusto London Junior
Grantee:Julio Augusto Druzina Massignan
Home Institution: Escola de Engenharia de São Carlos (EESC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Associated scholarship(s):18/00214-4 - Distribution systems state estimators: classical approaches and new algorithms for the Smart Grid, BE.EP.DR


Due to the development of the advanced metering infrastructure, and other equipment and smart sensors, the amount of measurements have been growing in electrical Distribution Systems (DS). This and other factors have been motivating the development of State Estimators (SE) for real time monitoring of these systems, ensuring a reliable database for the implementation of other automatic features of the so called Smart Grids concept. However, the majority of SEs developed for DS does not address all the particular characteristics of these systems, and the few that address, does not present a full methodology to deal with all the concepts of Smart Grids and computational efficiency for application in large scale systems, generally applying their methodologies in a few small benchmarking test feeders. This research project has the goal of developing and implementing a Three-Phase Multiarea State Estimator for Distribution Systems to be applied in real large scale systems, treating the particular characteristics of these systems in the current technologies of DSs and the future concepts of Smart Grids. This way, the proposed SE must perform: the analysis of unbalanced and asymmetrical networks, with single, two and three-phase circuits, in radial or meshed topologies without loss of precision; a proper treatment in the pseudo-measurements modeling at the low voltage consumers level, considering the current models based on typical load profiles, and also the possibility of smart meters and distributed generation at these low voltage consumers; a proper treatment of several types of measurements (virtual, pseudo-measurements, non-synchronized conventional measurements and phasor measurements units); the application of sparse matrix techniques and efficient numerical methods in the solution of the DS state estimation problem; a proper treatment for the lack of synchronism between the several types of measurements, such as between load estimated and load measured at the low voltage consumers and between the available measurements at the primary feeders and the substation measurements; the treatment of large real distribution systems with several feeders and substations. For the development of the proposed SE, several studies will be conducted, focusing mainly on the following topics: mathematical formulation of the SEs for DSs; efficient numerical methods and sparse matrix techniques applied to the state estimation problem; the inclusion of different types of measurements in the three-phase state estimation process, and the treatment of the lack of synchronism among the sampling rates of these measurements; modeling of pseudo-measurements considering the possibility of low voltage consumers monitored by smart meters; distributed generation penetration; context of large scale systems and the formulation of the multiarea SEs. (AU)

Scientific publications (6)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
BESSANI, MICHEL; MASSIGNAN, JULIO A. D.; SANTOS, TALYSSON M. O.; LONDON JR, JOAO B. A.; MACIEL, CARLOS D. Multiple households very short-term load forecasting using bayesian networks {*}. Electric Power Systems Research, v. 189, DEC 2020. Web of Science Citations: 0.
HEBLING, GUSTAVO M.; MASSIGNAN, JULIO A. D.; LONDON JUNIOR, JOAO B. A.; CAMILLO, MARCOS H. M. Sparse and numerically stable implementation of a distribution system state estimation based on Multifrontal QR factorization. Electric Power Systems Research, v. 189, DEC 2020. Web of Science Citations: 0.
MASSIGNAN, JULIO A. D.; LONDON, JR., JOAO B. A.; MIRANDA, VLADIMIRO. Tracking Power System State Evolution with Maximum-correntropy-based Extended Kalman Filter. JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY, v. 8, n. 4, p. 616-626, JUL 2020. Web of Science Citations: 0.
BESSANI, MICHEL; MASSIGNAN, JULIO A. D.; FANUCCHI, RODRIGO Z.; CAMILLO, MARCOS H. M.; LONDON, JOAO B. A.; DELBEM, ALEXANDRE C. B.; MACIEL, CARLOS D. Probabilistic Assessment of Power Distribution Systems Resilience Under Extreme Weather. IEEE SYSTEMS JOURNAL, v. 13, n. 2, p. 1747-1756, JUN 2019. Web of Science Citations: 0.
VIGLIASSI, MARCOS PAULO; MASSIGNAN, JULIO A. D.; DELBEM, ALEXANDRE CLAUDIO B.; LONDON, JR., JOAO BOSCO A. Multi-objective evolutionary algorithm in tables for placement of SCADA and PMU considering the concept of Pareto Frontier. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, v. 106, p. 373-382, MAR 2019. Web of Science Citations: 3.
DRUZINA MASSIGNAN, JULIO AUGUSTO; AUGUSTO LONDON, JR., JOAO BOSCO; BESSANI, MICHEL; MACIEL, CARLOS DIAS; CLAUDIO BOTAZZO DELBEM, ALEXANDRE; MARCAL CAMILLO, MARCOS HENRIQUE; DE LIMA SOARES, TELMA WOERLE. In-Field Validation of a Real-Time Monitoring Too for Distribution Feeders. IEEE Transactions on Power Delivery, v. 33, n. 4, p. 1798-1808, AUG 2018. Web of Science Citations: 2.

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