Sensitivity analysis by neural networks applied to power systems transient stability

This work presents a procedure for transient stability analysis and preventive control of electric power systems, which is formulated by a multilayer feedforward neural network. The neural network training is realized by using the back-propagation algorithm with fuzzy controller and adaptation of the inclination and translation parameters of the nonlinear function. These procedures provide a faster convergence and more precise results, if compared to the traditional back-propagation algorithm. The adaptation of the training rate is effectuated by using the information of the global error and global error variation. After finishing the training, the neural network is capable of estimating the security margin and the sensitivity analysis. Considering this information, it is possible to develop a method for the realization of the security correction (preventive control) for levels considered appropriate to the system, based on generation reallocation and load shedding. An application for a multimachine power system is presented to illustrate the proposed methodology. (c) 2006 Elsevier B.V. All rights reserved.

Control preventivo dinamico de sistemas de energia electrica usando redespacho de generacion y corte de carga

A model for preventive control in electrical systems is presented, taking into account the dynamic aspects of the network. Among these aspects, the effects provoked by perturbations which cause oscillations in synchronous machine angles (transient stability), such as electric equipment outages and short circuits, are presented. The energy function is used to measure the stability of the system using a procedure defined as the security margin. The control actions employed are load shedding and generation reallocation. An application of the methodology to a system located in southern Brazil, which is composed of 10 synchronous machines, 45 busses, and 72 transmission lines. The results confirm the theoretical studies.

Planeamiento de la expansión de sistemas de transmisión considerando contingencias y demanda incierta

This paper presents a methodology and a mathematical model to solve the expansion planning problem that takes into account the effect of contingencies in the planning stage, and considers the demand as a stochastic variable within a specified range. In this way, it is possible to find a solution that minimizes the investment costs guarantying reliability and minimizing future load shedding. The mathematical model of the expansion planning can be represented by a mixed integer nonlinear programming problem. To solve this problem a specialized Genetic Algorithm combined with Linear Programming was implemented.

Market-driven security-constrained transmission network expansion planning

This paper presents a Bi-level Programming (BP) approach to solve the Transmission Network Expansion Planning (TNEP) problem. The proposed model is envisaged under a market environment and considers security constraints. The upper-level of the BP problem corresponds to the transmission planner which procures the minimization of the total investment and load shedding cost. This upper-level problem is constrained by a single lower-level optimization problem which models a market clearing mechanism that includes security constraints. Results on the Garver's 6-bus and IEEE 24-bus RTS test systems are presented and discussed. Finally, some conclusions are drawn. © 2011 IEEE.

An application of a modified constructive heuristic algorithm to transmission expansion planning

Transmission expansion planning (TEP) is a non-convex optimization problem that can be solved via different heuristic algorithms. A variety of classical as well as heuristic algorithms in literature are addressed to solve TEP problem. In this paper a modified constructive heuristic algorithm (CHA) is proposed for solving such a crucial problem. Most of research papers handle TEP problem by linearization of the non-linear mathematical model while in this research TEP problem is solved via CHA using non-linear model. The proposed methodology is based upon Garver's algorithm capable of applying to a DC model. Simulation studies and tests results on the well known transmission network such as: Garver and IEEE 24-bus systems are carried out to show the significant performance as well as the effectiveness of the proposed algorithm. © 2011 IEEE.

Redespacho de reativos como ação corretiva para alívio de sobrecargas em Redes de transmissão

Pós-graduação em Engenharia Elétrica - FEIS

Análise de sensibilidade por redes neurais para estudos da estabilidade transitória de sistemas elétricos de potência

Pós-graduação em Engenharia Elétrica - FEIS

A Multi-Stage Stochastic Non-Linear Model for Reactive Power Planning Under Contingencies

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Planejamento de reativos em sistemas elétricos de potência multi-área através de modelos estocásticos

Pós-graduação em Engenharia Elétrica - FEIS

Algoritmo tabu search especializado para o problema de planejamento da expansao de sistemas de transmissão

Pós-graduação em Engenharia Elétrica - FEIS

Reactive power planning under conditional-value-at-risk assessment using chance-constrained optimisation

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Experimental infection of Newcastle disease virus in pigeons (Columba livia): Humoral antibody response, contact transmission and viral genome shedding

The aim of this study was to evaluate the humoral antibody response, the genome viral excretion and the contact transmission of pathogenic chicken origin Newcastle disease virus (NDV) from experimentally infected pigeons (Columba livia) to in-contact pigeon. The antibody response to infection was assessed by the hemagglutination inhibition (HI) test and the genome viral excretion was detected by RT-PCR. Viral strain induced high antibody levels, both in inoculated and in sentinel birds. The pathogenic viral strain for chickens was unable to produce clinical signs of the disease in experimentally infected pigeons, although it induced the Immoral antibody response and produced NDV genome shedding. NDV genome was detected intermittently throughout the experimental period, from 5 days post-infection (dpi) to 24 dpi. Therefore, viral genome shedding occurred for 20 days. The viral genome was detected in all birds, between I I and 13 dpi. Furthermore, the high infectivity of the virus was confirmed, as all non-inoculated sentinel pigeons showed antibody levels as high as those of inoculated birds. (C) 2007 Elsevier B.V. All rights reserved.

A novel neural model to electrical load forecasting in transformers

The paper describes a novel neural model to electrical load forecasting in transformers. The network acts as identifier of structural features to forecast process. So that output parameters can be estimated and generalized from an input parameter set. The model was trained and assessed through load data extracted from a Brazilian Electric Utility taking into account time, current, tension, active power in the three phases of the system. The results obtained in the simulations show that the developed technique can be used as an alternative tool to become more appropriate for planning of electric power systems.

An efficient neural approach to economic load dispatch in power systems

A neural approach to solve the problem defined by the economic load dispatch in power systems is presented in this paper, Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements the ability of neural networks to realize some complex nonlinear function makes them attractive for system optimization the neural networks applyed in economic load dispatch reported in literature sometimes fail to converge towards feasible equilibrium points the internal parameters of the modified Hopfield network developed here are computed using the valid-subspace technique These parameters guarantee the network convergence to feasible quilibrium points, A solution for the economic load dispatch problem corresponds to an equilibrium point of the network. Simulation results and comparative analysis in relation to other neural approaches are presented to illustrate efficiency of the proposed approach.

Neural Network-Based Approach for Identification of the Harmonic Content of a Nonlinear Load in a Single-Phase System

In this paper an alternative method based on artificial neural networks is presented to determine harmonic components in the load current of a single-phase electric power system with nonlinear loads, whose parameters can vary so much in reason of the loads characteristic behaviors as because of the human intervention. The first six components in the load current are determined using the information contained in the time-varying waveforms. The effectiveness of this method is verified by using it in a single-phase active power filter with selective compensation of the current drained by an AC controller. The proposed method is compared with the fast Fourier transform.