Methodologies for diagnosing and recovering smart meter data for advanced management of electric power distribution systems

• Grant number: 25/01669-9
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: April 01, 2025
• End date: May 31, 2027
• Field of knowledge: Engineering - Electrical Engineering - Power Systems
• Principal Investigator: Fernanda Caseño Trindade Arioli
• Grantee: Nelson Rodrigues Silva
• Host Institution: Faculdade de Engenharia Elétrica e de Computação (FEEC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated research grant: 21/11380-5 - CPTEn - São Paulo Center for the Study of Energy Transition, AP.CCD

Abstract

In modern electricity distribution networks, electromechanical meters have been gradually replaced by electronic meters with communication, the so-called smart meters, allowing greater observability over these systems, benefiting management practices. Considering the functionalities present in smart meters, a range of new solutions or improvements to existing solutions emerge. However, due to commercially viable measurement and communication methodologies, the volume of missing or inaccurate data tends to be significant (25%-40%), making it impossible to extract the full potential of the available quantities. In this context, this PhD project proposes the development of techniques for diagnosing, recovering and treating data errors to enable applications of interest to distribution utilities and customer units. It is worth noting that the research group has real measurement data from smart meters installed in 100% of consumers in a city in the state of São Paulo with more than 20,000 consumer units collected over a period of 1 year. Among the applications of interest to utilities, advanced management functions for electricity distribution systems stand out, such as loss management, voltage control and reactive power compensation, fault management, etc. In the case of customer units, it is possible to automatically and more accurately monitor power interruptions, transgressions and voltage variations that often affect their electronic equipment, as well as automatically suggest maintenance and/or equipment update actions to reduce electricity costs and improve installation efficiency.