Dedução de Funções de Alívio para Priorização de Variantes de Chaveamento Adequadas à Eliminação de Sobrecargas

Eventually, violations of voltage limits at buses or admissible loadings of transmission lines and/or power transformers may occur by the power system operation. If violations are detected in the supervision process, corrective measures may be carried out in order to eliminate them or to reduce their intensity. Loading restriction is an extreme solution and should only be adopted as the last control action. Previous researches have shown that it is possible to control constraints in electrical systems by changing the network topology, using the technique named Corrective Switching, which requires no additional costs. In previous works, the proposed calculations for verifying the ability of a switching variant in eliminating an overload in a specific branch were based on network reduction or heuristic analysis. The purpose of this work is to develop analytical derivation of linear equations to estimate current changes in a specific branch (due to switching measures) by means of few calculations. For bus-bar coupling, derivations will be based on short-circuit theory and Relief Function methodology. For bus-bar splitting, a Relief Function will be derived based on a technique of equivalent circuit. Although systems of linear equations are used to substantiate deductions, its formal solution for each variant, in real time does not become necessary. A priority list of promising variants is then assigned for final check by an exact load flow calculation and a transient analysis using ATP Alternative Transient Program. At last, results obtained by simulation in networks with different features will be presented

Determinação dos parâmetros do controlador preditivo generalizado com função de pesos variante no tempo através de algoritmos genéticos

The predictive control technique has gotten, on the last years, greater number of adepts in reason of the easiness of adjustment of its parameters, of the exceeding of its concepts for multi-input/multi-output (MIMO) systems, of nonlinear models of processes could be linearised around a operating point, so can clearly be used in the controller, and mainly, as being the only methodology that can take into consideration, during the project of the controller, the limitations of the control signals and output of the process. The time varying weighting generalized predictive control (TGPC), studied in this work, is one more an alternative to the several existing predictive controls, characterizing itself as an modification of the generalized predictive control (GPC), where it is used a reference model, calculated in accordance with parameters of project previously established by the designer, and the application of a new function criterion, that when minimized offers the best parameters to the controller. It is used technique of the genetic algorithms to minimize of the function criterion proposed and searches to demonstrate the robustness of the TGPC through the application of performance, stability and robustness criterions. To compare achieves results of the TGPC controller, the GCP and proportional, integral and derivative (PID) controllers are used, where whole the techniques applied to stable, unstable and of non-minimum phase plants. The simulated examples become fulfilled with the use of MATLAB tool. It is verified that, the alterations implemented in TGPC, allow the evidence of the efficiency of this algorithm

Estudo e implementação de um controlador preditivo generalizado bilinear compensado adaptativo

The present work presents the study and implementation of an adaptive bilinear compensated generalized predictive controller. This work uses conventional techniques of predictive control and includes techniques of adaptive control for better results. In order to solve control problems frequently found in the chemical industry, bilinear models are considered to represent the dynamics of the studied systems. Bilinear models are simpler than general nonlinear model, however it can to represent the intrinsic not-linearities of industrial processes. The linearization of the model, by the approach to time step quasilinear , is used to allow the application of the equations of the generalized predictive controller (GPC). Such linearization, however, generates an error of prediction, which is minimized through a compensation term. The term in study is implemented in an adaptive form, due to the nonlinear relationship between the input signal and the prediction error.Simulation results show the efficiency of adaptive predictive bilinear controller in comparison with the conventional.