Estimação de estados de distorções harmônicas em sistemas elétricos de potência utilizando estratégias evolutivas.

Abstract

The aim of the present thesis is to develop a Harmonic Distortion State Estimation algorithm for Power Systems. Although the cost of Power Quality Meters has decreased for a while now, it is still not practicable the use of one meter for each real system bus. This demands a Harmonic Distortion State Estimation (HDSE) algorithm to find the harmonic components in buses with no measurement. An Evolutionary Strategy algorithm is proposed in this work to solve the harmonic estimation problem. This algorithm shows good results due to its easy implementation and its quickness to find a solution for a complex problem, as the HDSE demands. In this context a solution for the estimation problem is based on a population of individuals that suffer mutation and recombination operations, and are evaluated in each generation. The individual evaluation is related to the minimization of the sum of square deviations of voltage estimation in buses with measurement. Harmonic data from meters and network data are taken as the model inputs. A load flow determines the system state for the fundamental frequency that provides the reference for the angle adjustment for harmonics.The proposed algorithm was first submitted to analyze a small four bus electrical network to verify the viability of this methodology. After this, a 14 bus electrical network was used to configure the evolutionary strategy algorithm parameters and to determine the Total Harmonic Distortion estimation. Finally, in order to understand the limitations and capability of the proposed methodology, a 50 bus subtransmission three-phase network was analyzed by models based on symmetrical components and phase components. The obtained results are very promising and indicate a considerable cost reduction in power quality monitoring systems.

Localização de faltas em redes de distribuição no contexto de redes elétricas inteligentes utilizando algoritmos evolutivos.

“…Neste trabalho, apresentam-se duas das principais abordagens para algoritmos evolutivos (ARRUDA, 2008):…”