Navegando por Assunto "Parametric identification"

Agora exibindo 1 - 3 de 3

Acesso aberto (Open Access)
Cálculo de equivalentes dinâmicos de sistema de potência usando algoritmos genéticos
(Universidade Federal do Pará, 2014-08-07) SANTOS, Pitther Negrão dos; BEZERRA, Ubiratan Holanda; http://lattes.cnpq.br/6542769654042813; VIEIRA, João Paulo Abreu; http://lattes.cnpq.br/8188999223769913
This dissertation presents a genetic algorithm-based method for calculating of power system dynamic equivalents aiming to represent parts of a power system for transient stability analysis. The dynamic equivalent calculation is obtained through carrying out the parameter identification of synchronous generators located on frontier buses, linking the external and the studied subsystem. An index is used to assess the proximity between simulations carried out using the full and the reduced model following the large disturbances emerged in the studied subsystem. Different operating conditions are taken into account. The simulations were conducted using the softwares GAOT “The Genetic Algorithm Optimization Toolbox”,, ANAREDE and ANATEM. This method is tested on a Kundur’s two-area test system and on a Brazilian Interconnect Power System (BIPS). Test results validate the efficacy of the developed methodology in calculating the robust dynamic equivalents.
Acesso aberto (Open Access)
Estratégia de identificação paramétrica aplicada à modelagem fenomenológica de um sistema do tipo correia transportadora industrial para fins de detecção de faltas
(Universidade Federal do Pará, 2014-07) MEDEIROS, Renan Landau Paiva de; BARRA JUNIOR, Walter; http://lattes.cnpq.br/0492699174212608
Belt conveyor systems are essential for large companies, even though this equipment has a great deal of importance that makes its non-planned stop can generate huge amounts of losses or even the stall of the whole production process. Having this importance in mind it becomes necessary to realize the adequate monitoring of the system and detect with a larger prior notice the occurrence of some fault in the system. In an effort to reduce the unforeseen stop, this dissertation investigates a modeling of a belt conveyor system. At first a phenomenological model of the process is discussed based on the mechanical laws and considering the diverse types of movement opposition force throughout the belt conveyor. The main parameters of a belt conveyor belt where estimated through the non-recursive mean square. In sequence a fault detection algorithm was elaborated using the interval analysis theory, in a way that its possible to detect inadequate operation conditions. With the intent to evaluate the performance of the proposed algorithm a prototype that emulates the tipical operation of a conveyor belt was designed. The results were obtained experimentally, that confirms the great performance of the proposed methodology.
Acesso aberto (Open Access)
Metodologia baseada em sistema fuzzy intervalar do tipo-2 para detecção e identificação de faltas de incipientes em motores de indução
(Universidade Federal do Pará, 2013-02-27) ROCHA, Erick Melo; BARRA JUNIOR, Walter; http://lattes.cnpq.br/0492699174212608
Since the incorporation of automation in the production processes, aiming at order to improve productivity and quality of products and services, researches on more efficient methodologies for fault diagnosis became more intensive. Such techniques allow the early detection of faults, before then lead to failures. This work investigates techniques for detection and diagnosis of faults and its application to induction motors, limiting their study to two situations, namely: system free of faults and system under incipient partial short-circuit in the coils the stator winding. For faults detection, parametric analysis of fist order ARX (autoregressive with exogenous input) were applied. The parameters of identified ARX modes, which bring information about the dynamics of the dominant system, are recursively obtained by the techniques of recursive least squares (RLS). In order to evaluate the capability for early fault detection, a type-2 interval fuzzy system was developed. This kind of fuzzy system has capability to capture a larger set of uncertainties than conventional (type-1) fuzzy systems. The footprint of uncertainty (FOU), characteristic of type-2 fuzzy system, is a way to accounts for uncertainties coming from noise and numerical errors from the process of parameter estimation. The ARX model parameters are the inputs to the supervisor system. Genetic algorithms (GA’s) were used for optimization of SIF interval type-2, aiming at to reduce the diagnostic error. The results obtained in tests of computer simulation show the effectiveness of the proposed methodology.