Diseño de un rele neuronal de protección para lineas aéreas de AT con preprocesado de señal mediante la transformada Wavelet

Abstract

The great advance of the microprocessor and computer technology has allowed the electromechanical and analog protection devices to be substituted by digital ones. However, the algorithms used in digital protections have some disadvantages related to the errors caused by current transformer saturation, voltage transformer ferro-resonance or by dynamics arcing faults. Over the past 10 years there have been some advances in the application of artificial intelligence, especially of neural networks, on power system analysis. These applications were useful in the prediction of optimum load flow and fault pattern classification. The term pattern classification encompasses a wide range of information processing problems of great practical significance. Often the framework in which to formulate solutions to pattern recognition is the statistical one, with the develop of an algorithm to be able to correctly classify a previously unseen pattern. One approach to statistical pattern recognition is using Bayes¿ theorem, which gives the probability of the pattern belonging to a particular class, and expresses this posterior probability in terms of previous measurements. The space is divided up into decision regions depending on the observed features. The boundaries between regions are known as decision boundaries. The outputs of a neural network can be interpreted as posterior probabilities calculated with the error function (the network training is based on maximum likelihood which is equivalent to minimization of an error function), and the neural network approximates the decision boundaries of the Bayes¿ theorem. For these reasons and because of the advantage of the parallel processing capabilities of the neural networks, our objective here is to search for new applications of neural networks on power system protection in order to improve it and solve the problems mentioned above. In order to do this in this thesis, the artificial neural networks are applied to the protection of high voltage transmission lines, assuming dynamic arcing faults, the current transformer saturation, the change of the short circuit levels, the change of the power network topology, the bad and noisy data, and sudden load changes. The methodology carried out in a special training process of the neural networks, using protection relays by means of simulated faults data, obtained by the electromagnetic transient program (EMTDC/PSCAD) applied to a generalized power network model which includes all the possible topologies and load levels. The training algorithm was implemented by the author in the C++ language program. Within this project we included the study and application of the wavelet transform, which showed a series of advantages in front of traditional methods in signal processing. In traditional power signal analysis tools, among several algorithms, the Fourier transform has been used as well as the Kalman filtering, etc. However, in presence of non stationary signals the performance of these techniques are limited. Thus, if there is a local transient over some small interval of time in the lifetime of the signal, the transient will contribute to the Fourier transform, but its location on the time axis will be lost. Wavelets analysis overcomes this limitation by employing an analyzing function that is local both in time and frequency. The wavelet transform is a powerful tool for the study and analysis of transient phenomena in electric power systems. Furthermore, in this work the wavelet transform has been implemented with a neural network which allows us to incorporate it within the whole network for pattern recognition. Both neural networks along with the information supplied by the wavelets, have made it possible to increase the speed and reliability of the relay, especially during the fault inception. Also, one of our aims was to carry out the practical implementation of an electronic board in order to test the performance of the neural relay. To do this, it was necessary to use equipment to carry out the laboratory test, by emulating the same or a similar system employed while designing and training the relay. Prof. R. K. Aggarwal, in the Electrical department in the University of Bath expertise in the application of signal processing and Artificial Intelligence technology for the development of novel intelligent relays for Power Systems, both for transmission and distribution systems. Their experience is not only in the CAD work but also in the design and engineering of proto-type hardware. To do this the RTDS (Real Time Digital Simulator) system was employed. The obtained results were fully satisfactory. These successful tests results encourage investment in neural networks in high speed protec-tion for high voltage transmission lines. The response time is even less than 1.8 ms for faults close to the relay, which has a good performance in front of noise, faulty data and sudden change of load.