Development of data-driven metodology for improvement of pperation of EPS with high penetration of wind/solar generation

• Grant number: 19/10033-0
• Support Opportunities: Regular Research Grants
• Start date: September 01, 2019
• End date: February 28, 2022
• Field of knowledge: Engineering - Electrical Engineering - Power Systems
• Principal Investigator: Daniel Dotta
• Grantee: Daniel Dotta
• Host Institution: Faculdade de Engenharia Elétrica e de Computação (FEEC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• City of the host institution: Campinas

Abstract

Electric power systems (ESS) have always been characterized, considering the large number of components and devices, due to their high size and complexity. Recently, this complexity has grown exponentially, characterized by the effort to make it sustainable in the long term with the significant increase in the share of wind and solar energy in the energy system. The goal is to replace fossil and nuclear generation plants in such a way as to achieve 100% renewable generation. However, due to the characteristics and complexity (power electronics technology-based systems) of wind and photovoltaic generators, their interaction with the grid is significantly different from that of traditional generators. As an example, most wind and solar generators have no rotating parts and do not naturally contribute to improving the inertial response of the SEE. As a consequence, in the case of load / generation unbalance disturbances, ESAs become more susceptible to sudden frequency variations. In addition to these problems, the complexity of large-scale ESS modeling is practically unfeasible, and analytical and design techniques based exclusively on mathematical-computational models lose effectiveness. In this context, data-based techniques and methodologies represent an alternative potential to face the operational challenges of the SEE of the future. With the worldwide popularization of WAMS (Wide Area Measurement System) systems, which are now installed in the main SEE operating centers, a basic infrastructure for the development of new data-based analysis and control applications emerges. WAMS systems allow observation of phenomena with high resolution and synchronization from measurements performed at different points of the SEE. A further advantage is that this large number of measures is available in easily accessible databases located in the operating centers. It should be noted that the analysis of the large amount of data produced by the WAMS systems is not feasible, if done manually. Considering the high volume, sampling rate and variety of data collected by the WAMS system, an ideal condition for the application of machine learning techniques and / or based exclusively on the information contained in data is obtained. Specifically, in this research project, the following objectives are sought: a) implement a real-time system for the detection and identification of disorders in SEE; b) development of methods capable of extracting information about the inertial contents of the system from synchrophasors; c) develop data-driven control synthesis tools. The final result is to evaluate the developed applications, verifying their ability to improve the operational safety of future electrical networks. (AU)

Scientific publications

(14)

(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)

LUGNANI, LUCAS; PATERNINA, MARIO R. ARRIETA; DOTTA, DANIEL; CHOW, JOE H.; LIU, YILU. ower System Coherency Detection From Wide-Area Measurements by Typicality-Based Data Analysi. IEEE Transactions on Power Systems, v. 37, n. 1, p. 388-401, JAN 2022. (19/10033-0, 18/07375-3, 16/08645-9)

GARCIA, MAIQUE C.; DOTTA, DANIEL; PEREIRA, LEANDRO; DE ALMEIDA, MADSON C.; SANTOS, ORLEM L. D.; DA SILVA, LUIZ C. P.; IEEE. Design and Development of D-PMU Module for Smart Meters. 2020 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM), v. N/A, p. 5-pg., 2020-01-01. (16/08645-9, 19/10033-0)

FERNANDES, LUCAS L.; JONES, MORGAN; ALBERTO, LUIS; PEET, MATTHEW; DOTTA, DANIEL. Combining Trajectory Data With Analytical Lyapunov Functions for Improved Region of Attraction Estimation. IEEE CONTROL SYSTEMS LETTERS, v. 7, p. 6-pg., 2022-06-30. (18/07375-3, 19/10033-0)

LUGNANI, LUCAS; DOTTA, DANIEL; LACKNER, CHRISTOPH; CHOW, JOE. ARMAX-based method for inertial constant estimation of generation units using synchrophasors. Electric Power Systems Research, v. 180, MAR 2020. (19/10033-0, 16/08645-9, 18/07375-3)

LUGNANI, LUCAS; DOTTA, DANIEL; PATERNINA, MARIO R. A.; CHOW, JOE; NORDSTROM, L; HOLJEVAC, N; KUZLE, I; IVANKOVIC, I; KEZUNOVIC, M; PAULONE, M; et al. Real-time Coherency Identification using a Window-Size-Based Recursive Typicality Data Analysis. 2022 INTERNATIONAL CONFERENCE ON SMART GRID SYNCHRONIZED MEASUREMENTS AND ANALYTICS - SGSMA 2022, v. N/A, p. 6-pg., 2022-01-01. (16/08645-9, 18/20104-9, 19/10033-0, 18/07375-3)

ALCAHUAMAN, HEVER; LOPEZ, JUAN CAMILO; DOTTA, DANIEL; RIDER, MARCOS J.; GHIOCEL, SCOTT. Optimized Reactive Power Capability of Wind Power Plants With Tap-Changing Transformers. IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, v. 12, n. 4, p. 1935-1946, OCT 2021. (16/08645-9, 19/08200-5, 15/21972-6, 19/01906-0, 17/21752-1, 19/10033-0, 18/20104-9, 17/25425-5)

PINHEIRO, BRUNO; LUGNANI, LUCAS; DOTTA, DANIEL; IEEE. A Procedure for the Estimation of Frequency Response using a Data-Driven Method. 2021 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM), v. N/A, p. 5-pg., 2021-01-01. (18/20104-9, 19/08200-5, 16/08645-9, 19/10033-0, 17/25425-5)

FERNANDES, LUCAS L.; PATERNINA, MARIO R. ARRIETA; DOTTA, DANIEL; CHOW, JOE H.. Data-driven assessment of center of inertia and regional inertia content considering load contribution. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, v. 156, p. 13-pg., 2023-12-20. (16/08645-9, 18/20104-9, 19/10033-0, 18/07375-3)

LOPES, GABRIEL V. DE S.; MORAES, GUIDO R.; ISSICABA, DIEGO; DOTTA, DANIEL. WAMS-based two-level robust detection methodology of power system events. SUSTAINABLE ENERGY GRIDS & NETWORKS, v. 31, p. 13-pg., 2022-09-01. (19/08200-5, 19/10033-0, 18/20104-9, 17/25425-5, 16/08645-9)

DOS SANTOS, LUCAS E.; HUAMAN, JESUS F.; QUEIROZ, ELIABE D.; DOTTA, DANIEL; POMILIO, JOSE A.. A MIMO Self-Tuning Data-Driven Control Application for Power Electronic Converters Robustness Operation. 2024 IEEE 9TH SOUTHERN POWER ELECTRONICS CONFERENCE, SPEC, v. N/A, p. 7-pg., 2024-01-01. (18/07375-3, 19/10033-0, 21/11258-5, 16/08645-9)

BERNARDO, RAUL T.; CAMPESTRINI, LUCIOLA; OLIVEIRA, RICARDO C. L. F.; AQUINO, ANTONIO F. C.; DOTTA, DANIEL. A Data-driven Approach for the Design of Wide-Area Damping Controllers. JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS, v. 34, n. 5, p. 15-pg., 2023-08-08. (18/07375-3, 16/08645-9, 19/10033-0)

SANTOS, ORLEM L. D.; DOTTA, DANIEL; WANG, MENG; CHOW, JOE H.; DECKER, ILDEMAR C.. Performance analysis of a DNN classifier for power system events using an interpretability method. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, v. 136, p. 15-pg., 2022-03-01. (19/08200-5, 18/20104-9, 17/25425-5, 16/08645-9, 19/10033-0)

ORTIZ-BEJAR, JOSE; ZAMORA-MENDEZ, ALEJANDRO; LUGNANI, LUCAS; TELLEZ, ERIC; PATERNINA, MARIO R. ARRIETA. Power system coherency assessment by the affinity propagation algorithm and distance correlation. SUSTAINABLE ENERGY GRIDS & NETWORKS, v. 30, p. 11-pg., 2022-06-01. (16/08645-9, 19/10033-0, 18/07375-3)

GARCIA, MAIQUE C.; DOTTA, DANIEL; PEREIRA, LEANDRO; DE ALMEIDA, MADSON C.; PATERNINA, MARIO R. A.; DOS SANTOS, ORLEM LIMA; DA SILVA, LUIZ C. P.; ALVES JUNIOR, JOSE EDUARDO DA R.. A Development PMU Device for Living Lab Applications. JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS, v. 32, n. 4, p. 12-pg., 2021-05-08. (16/08645-9, 19/10033-0, 18/20104-9)