Alocação de Medidores para a Estimação de Estado em Redes Elétricas Inteligentes

• Autor(a) principal: Raposo, Antonio Adolpho Martins
• Data de Publicação: 2016
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UFMA
• Texto Completo: http://tedebc.ufma.br:8080/jspui/handle/tede/299
• topic: Redes de Distribuição; Estimação de Estado; Simulação Monte Carlo; Variáveis Aleatórias Correlacionadas; Distribuição Normal Bivariada; Alocação de Medidores; Distribution Networks; State Estimation; Monte Carlo Simulation; Correlated Random Variables; Bivariate Normal Distribution; Meter Placement; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA

To plan and operate properly a Smart Grid (SG), many new technical considerations in the context of distribution systems, must be considered, for example: stability (due to installation of Distributed Generation (DG), the load and generation dispatch, management of energy storage devices and the assessment of the impact of electric vehicle connection on the distribution system. The main prerequisite for many of these new functions in the distribution system control center is to determine the electrical network state (magnitude and angle of nodal voltages) in real time from measurement devices installed in it. In the transmission system control centers, this task is performed by the state estimation tool. Thus, the Distribution System State Estimation (DSSE) is one of the cornerstones for the implementation of a SG. The presence of a small number of measurements can make the grid unobservable in the context of the DSSE. That is, the state variables (magnitude and angle of the node voltages of all bus) can not be determined from a set of measurements by a state estimator. Due to this, it is usually added a large number of pseudo measurements to the existing measurement plan to ensure observability and to enable the DSSE. A drawback with this strategy is that the accuracy of the estimated state is compromised due to the fact that the errors associated with the pseudo measurements are considerably higher than those relating to real measurements. Consequently, it is necessary to allocate meters (voltage magnitude, active and reactive power flows, current magnitudes, etc.) to guarantee the accuracy of the DSEE. The meter placement problem for the state estimation in the transmission networks is usually carried out with the objective of assuring the observability. On the other hand, the meter placement for the EERD aims to minimize probabilistic index associated with the errors between the true and estimated state vectors. An important component of the method used to solve the meters placement problem is a probabilistic technique used to estimate the objective function. Due to the nonlinear nature of DSSE problem, the best option has been to use the Monte Carlo Simulation (MCS). A disadvantage of the MCS to estimate the objective function of the allocation problem is its high computational cost due to the need to solve a nonlinear state estimation problem for each sample element. The main objective of this dissertation is to propose a probabilistic techniques to improve the computational performance of existing methodologies for meter placement without reducing the accuracy of the estimated ix state. This compromise has been established using two strategies. In the first one, a linear model is used to estimate the state and the MCS is applied to determine the risks of the objective function. In the second one, a closed analytical formula is used to determine the risks based on the linearized model. Furthermore, the improved versions of the meter placement algorithms proposed in this dissertation consider the effect of the correlation among the measurements. The proposed meter placement algorithms were tested in the British distribution system of 95 bus. The tests results demonstrate that the introduction of the proposed strategies in a meter placement algorithm significantly reduced its computational cost. Moreover, it can be observed that there were improvements in accuracy in some cases, because the risk estimates provided by MCS are not accurate with small samples.