| 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)
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