Volt-VAR multiobjective optimization to peak-load relief and energy efficiency in distribution networks

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

Previsão de carga multinodal utilizando redes neurais de regressão generalizada

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Controle de tensão e reativos em redes de distribuição de energia elétrica

Pós-graduação em Engenharia Elétrica - FEIS

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.

Electric load forecasting using a fuzzy ART&ARTMAP neural network

This work presents a neural network based on the ART architecture ( adaptive resonance theory), named fuzzy ART& ARTMAP neural network, applied to the electric load-forecasting problem. The neural networks based on the ARTarchitecture have two fundamental characteristics that are extremely important for the network performance ( stability and plasticity), which allow the implementation of continuous training. The fuzzy ART& ARTMAP neural network aims to reduce the imprecision of the forecasting results by a mechanism that separate the analog and binary data, processing them separately. Therefore, this represents a reduction on the processing time and improved quality of the results, when compared to the Back-Propagation neural network, and better to the classical forecasting techniques (ARIMA of Box and Jenkins methods). Finished the training, the fuzzy ART& ARTMAP neural network is capable to forecast electrical loads 24 h in advance. To validate the methodology, data from a Brazilian electric company is used. (C) 2004 Elsevier B.V. All rights reserved.

Multinodal Load Forecasting in Power Electric Systems using a Neural Network with Radial Basis Function

In this paper we present the results of the use of a methodology for multinodal load forecasting through an artificial neural network-type Multilayer Perceptron, making use of radial basis functions as activation function and the Backpropagation algorithm, as an algorithm to train the network. This methodology allows you to make the prediction at various points in power system, considering different types of consumers (residential, commercial, industrial) of the electric grid, is applied to the problem short-term electric load forecasting (24 hours ahead). We use a database (Centralised Dataset - CDS) provided by the Electricity Commission de New Zealand to this work.

A fast electric load forecasting using adaptive neural networks

This work presents a procedure for electric load forecasting based on adaptive multilayer feedforward neural networks trained by the Backpropagation algorithm. The neural network architecture is formulated by two parameters, the scaling and translation of the postsynaptic functions at each node, and the use of the gradient-descendent method for the adjustment in an iterative way. Besides, the neural network also uses an adaptive process based on fuzzy logic to adjust the network training rate. This methodology provides an efficient modification of the neural network that results in faster convergence and more precise results, in comparison to the conventional formulation Backpropagation algorithm. The adapting of the training rate is effectuated using the information of the global error and global error variation. After finishing the training, the neural network is capable to forecast the electric load of 24 hours ahead. To illustrate the proposed methodology it is used data from a Brazilian Electric Company. © 2003 IEEE.

Short-term multinodal load forecasting in distribution systems using general regression neural networks

Multinodal load forecasting deals with the loads of several interest nodes in an electrical network system, which is also known as bus load forecasting. To perform this demand, it is necessary a technique that is precise, trustable and has a short-time processing. This paper proposes two methodologies based on general regression neural networks for short-term multinodal load forecasting. The first individually forecast the local loads and the second forecast the global load and individually forecast the load participation factors to estimate the local loads. To design the forecasters it wasn't necessary the previous study of the local loads. Tests were made using a New Zealand distribution subsystem and the results obtained are compatible with the ones founded in the specialized literature. © 2011 IEEE.

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.

Effect of abutment's height and framework alloy on the load distribution of mandibular cantilevered implant-supported prosthesis

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

An investigation of pile diameter influence in the bearing capacity on Dynamic Load Test (DLT)

During the construction of five residential buildings in the city of Taubate, State of São Paulo, it was possible to carry out one comprehensive investigation of the behavior of precast concrete piles in clay shales. This paper describes the results of Dynamic Load Tests (DLT's) executed in three piles with different diameters and with the same embedded length. The tests were monitored using the PDA(R) (Pile Driving Analyzer) and the pile top displacement was measured by pencil and paper procedure. From the curves of RMX versus DMX resulted from CASE(R) method, CAPWAPC(R) analyses were made for signals where the maximum mobilized soil resistance was verified. The results were compared with the predicted bearing capacity using the semi-empirical method of Decourt & Quaresma (1978) and Decourt (1982) based on SPT values and the description of the soil profile. Some comments related to the values of quake and damping used for clay shales in the analyses are also presented.

Experience gained and difficulties in performing dynamic load test in composite piles made with steel rails

The results of dynamic instrumentation in two construction sites that used steel rails as pile foundation are presented in this paper. The first dynamic load tests were executed in piles made by single steel rails of the type TR32 and TR37. In the second group of dynamic tests, the piles were made by a composition of two and three steel rails of the same type TR37. The difficulties in placing the sensors, the effect of hammer blow eccentricity and the influence of the non-uniform welding along the pile length are presented in detail and discussed.

Influences of thermal and electric load fluctuations in the cogeneration attractiveness

The use of mean values of thermal and electric demand can be justifiable for synthesising the configuration and for estimating the economic results because it simplifies the analysis in a preliminary feasibility study of a cogeneration plant. For determining the cogeneration scheme that best fits the energetic needs of a process several cycles and combinations must be considered, and those technically feasible will be analysed according to economic models. Although interesting for a first approach, this procedure do not consider that the peaks and valleys present in the load patterns will impose additional constraints relatively to the equipment capacities. In this paper, the effects of thermal and electric load fluctuation to the cogeneration plant design were considered. An approach for modelling these load variability is proposed for comparing two competing thermal and electric parity competing schemes. A gas turbine associated to a heat recovery steam generator was then proposed and analysed for thermal- and electric-following operational strategies. Thermal-following option revealed to be more attractive for the technical and economic limits defined for this analysis. (c) 2006 Elsevier Ltd. All rights reserved.

Describing fatigue crack growth and load ratio effects in Al 2524 T3 alloy with an enhanced exponential model

The fatigue crack behavior in metals and alloys under constant amplitude test conditions is usually described by relationships between the crack growth rate da/dN and the stress intensity factor range Delta K. In the present work, an enhanced two-parameter exponential equation of fatigue crack growth was introduced in order to describe sub-critical crack propagation behavior of Al 2524-T3 alloy, commonly used in aircraft engineering applications. It was demonstrated that besides adequately correlating the load ratio effects, the exponential model also accounts for the slight deviations from linearity shown by the experimental curves. A comparison with Elber, Kujawski and "Unified Approach" models allowed for verifying the better performance, when confronted to the other tested models, presented by the exponential model. (C) 2012 Elsevier Ltd. All rights reserved.