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Medientyp:
E-Artikel
Titel:
NEURAL NETWORK CLASSIFIER OF ENERGY FACILITIES OPERATING MODES AND ITS RECOGNITION ABILITY ASSESSMENT AT DIFFERENT NUMBER OF PRECEDENTS
Beteiligte:
Dementiy, Yuri A.;
Maslov, Aleksandr N.
Erschienen:
I.N. Ulianov Chuvash State University, 2021
Erschienen in:Vestnik Chuvashskogo universiteta
Sprache:
Nicht zu entscheiden
DOI:
10.47026/1810-1909-2021-3-45-52
ISSN:
1810-1909
Entstehung:
Anmerkungen:
Beschreibung:
<jats:p>Classical algorithms of relay protection construction do not use all available information base and therefore cannot provide the highest possible sensitivity with guaranteed selectivity. These algorithms, as a rule, concentrate different information, as a result of which it is partially lost. For example, the resistance relay operates with complex resistance, that is, two real parameters, although two complex variables – voltage and current – are used to calculate the complex resistance. This paper shows the solution to the problem of classification of power line operating modes using a neural network algorithm. The simplest neural network, a perceptron, is a universal classifier, since a convergence theorem has been proved for it, showing that if a classification exists, a perceptron of sufficient complexity is able to describe it. The statistical and geometrical interpretations of various algorithms are discussed. The dependence of the quality of the classifier’s work on the distribution of precedents in the training sample, on which the training is based, as well as on the structure and parameters of the neural network, is shown. The recognition ability of the neural network classifier, i.e. the ability to distinguish short circuits within the protected zone from short circuits outside the protected zone at different number of precedents in the training sample, is evaluated. The limits of applicability of such algorithms to the task of classification of object operation modes in electric power industry are shown and recommendations for their practical application are formulated. The results obtained indicate the need to develop methods for training classifiers that are based on a source of informative precedents in the form of a simulation model of the object.</jats:p>