Stochastic estimation of maximum load to be restored by the self-healing system: a frequentist and Bayesian approach

• Grant number: 17/02196-0
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• Start date: July 24, 2017
• End date: July 23, 2018
• Field of knowledge: Engineering - Electrical Engineering - Power Systems
• Principal Investigator: Marcos Julio Rider Flores
• Grantee: Juan Camilo Lopez Amezquita
• Supervisor: Qiuwei Wu
• Host Institution: Faculdade de Engenharia Elétrica e de Computação (FEEC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Institution abroad: Technical University of Denmark (DTU), Denmark
• Associated to the scholarship: 15/12564-1 - Designing and Implementation of a Self-Healing Scheme for Modern Electrical Distribution Systems, BP.DR

Abstract

The main objective of this research project is to enhance the proposed centralize self-healing system by including a new statistical short-term load forecasting module. The aim of the new module is to estimate the maximum amount of load to be restored by the restoration algorithm after a permanent fault. Since the maximum amount of load at each zone is a time-variant random variable, whose probability distribution function is not known at the beginning of the restoration process, then the proposed model will be design as a statistical inference method that interactively models the random behavior of the load and predicts the maximum amount of load to be restored during contingency operation. (AU)

Scientific publications

(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)

LOPEZ, JUAN CAMILO; RIDER, MARCOS J.; WU, QIUWEI. Parsimonious Short-Term Load Forecasting for Optimal Operation Planning of Electrical Distribution Systems. IEEE Transactions on Power Systems, v. 34, n. 2, p. 1427-1437, MAR 2019. (17/02196-0)