Wind power is going through a very rapid development. It is among the fastest growing power sources in the world, the technology is being developed rapidly and wind power is supplying significant shares of the energy in large regions. The integration of wind power in the power system is now an issue in order to optimize the utilization of the resource and to continue the high rate of installation of wind generating capacity, which is necessary so as to achieve the goals of sustainability and security of supply. This paper presents the main technical challenges that are associated with the integration of wind power into power systems. These challenges include effects of wind power on the power system, the power system operating cost, power quality, power imbalances, power system dynamics, and impacts on transmission planning. The main conclusion is that wind power's impacts on system operating costs are small at low wind penetrations (about 5% or less). At higher wind penetrations, the impact will be higher, although current results suggest the impact remains moderate with penetrations approaching 20%. In addition, the paper presents the technology and expectations of wind forecasting as well as cases where wind power curtailment could arise. Future research directions for a better understanding of the factors influencing the increased integration of wind power into power systems are also provided.

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Technical challenges associated with the integration of wind power into power systems

With the fast-paced changing technologies in the power industry, new references addressing new technologies are coming to the market. Based on this fact, there is an urgent need to keep track of international experiences and activities taking place in the field of modern transformer design. The complexity of transformer design demands reliable and rigorous solution methods. A survey of current research reveals the continued interest in application of advanced techniques for transformer design optimization. This paper conducts a literature survey and reveals general backgrounds of research and developments in the field of transformer design and optimization for the past 35 years, based on more than 420 published articles, 50 transformer books, and 65 standards.

Transformer Design and Optimization: A Literature Survey

The complexity of transformer design demands reliable and rigorous solution methods. Survey of current research reveals the continued interest in application of advanced techniques for transformer design optimization. This paper summarizes some of the most important developments in this research area. The main purpose is to provide a synthesis of the published research in this field and stimulate further research interests and efforts in the respective topics.

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The state of the art in engineering methods for transformer design and optimization: a survey

The aim of the transformer design optimization is to define the dimensions of all the parts of the transformer, based on the given specification, using available materials economically in order to achieve lower cost, lower weight, reduced size, and better operating performance. In this paper, a hybrid artificial intelligence/numerical technique is proposed for the selection of winding material in power transformers. The technique uses decision trees and artificial neural networks for winding material classification, along with finite-element/boundary element modeling of the transformer for the calculation of the performance characteristics of each considered design. The efficiency and accuracy provided by the hybrid numerical model render it particularly suitable for use with optimization algorithms. The accuracy of this method is 96% (classification success rate for the winding material on an unknown test set), which makes it very efficient for industrial use

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Artificial Intelligence combined with Hybrid FEM-BE Techniques for Global Transformer Optimization

In order to obtain a good optimization method for the electrical transformer design with optimal selection of parameters, performance evaluation of three evolutionary algorithms (EAs), namely, genetic algorithm (GA), differential evolution algorithm, and nondominated sorting GA (NSGA-II), is carried out. The aim of this paper is to optimize parameters of transformer design (core thicknesses, primary-turn number, secondary-turn number, primary conductor area, and secondary conductor area) for minimization of total power losses (no-load losses and load losses) in three-phase transformer topology while maintaining high efficiency and low cost. The method used for this optimization scheme combines the finite-element method (FEM) and EAs to provide an accurate selection of parameters together with the optimized magnetic flux density and decreased loss. Experimental results show that NSGA-II+FEM model successfully provides a global feasible solution by minimizing total loss and related cost while improving the efficiency of three-phase transformer, rendering it suitable for application in the design environment of industrial transformers.

NSGA-II+FEM Based Loss Optimization of Three-Phase Transformer

Because the performance of power transformers is by various distinct parameters of the magnetic core, the prediction of relevant characteristics such as no-load losses P by analytical methods is impractical. This paper reports first attempts to predict the dependence of P on several parameters of core design by means of artificial neural networks (ANN's). Investigations of several ANN versions showed good results for simple backpropagation networks equipped with several output neurons for an adaptive version of Gaussian coarse coding. A main problem arises from the fact that an increase of input parameters is linked with a large increase in training batches established by time-consuming model core experiments. As a compromise, first ANN's were trained for the prediction of the losses P/sub J/ of "linearized" joint regions as a function of the most relevant parameters, including the number of overlap steps and the mean air-gap length of joints. This yields rough estimations of the joint's contribution to the building factor for small cores. For larger cores, an ANN cascade structure was tested. It includes a second ANN that considers indirect effect of joint designs on the global distribution of losses. The major problem with an ANN-based prediction system is establishing representative training data. Modified versions of the ANN method can be applied to various tasks, including the prediction of losses and noises of full-sized cores.

Th. Booth

Papers

Neural networks for the prediction of magnetic transformer core characteristics