Neural Networks in the Diagnostics Process of Low-Power Solar Plant Devices

Stanisław Duer,Jacek Paś,Jan Valicek,Radosław Duer,Marek Stawowy,Marcin Walczak,Dariusz Bernatowicz

doi:10.3390/en14092719

Abstract

The article presents the problems of diagnostics of low-power solar power plants with the use of the three-valued (3VL) state assessment {2, 1, 0}. The 3VL diagnostics is developed on the basis of two-valued diagnostics (2VL), and it is elaborated on. In the (3VL) diagnostics, the range of changes in the values of the signals from the 2VL logic was accepted for the serviceability condition: state {12VL}. This range of signal value changes for logic (3VL) was divided into two signal value change sub-ranges, which were assigned two status values in the logic (3VL): {23VL}—serviceability condition and {13VL}—incomplete serviceability condition. The state of failure for both logics applied of the valence of states is interpreted equally for the same changes in the values of diagnostic signals, the possible changes of which exceed the ranges of their permissible changes. The DIAG 2 intelligent system based on an artificial neural network was used in diagnostic tests. For this purpose, the article presents the structure, algorithm and rules of inference used in the DIAG intelligent diagnostic system. The diagnostic method used in the DIAG 2 system utilizes the method known from the literature to compare diagnostic signal vectors with the reference signal vectors assigned. The result of this vector analysis is the metric developed of the difference vector. The problem of signal analysis and comparison is carried out in the input cells of the neural network. In the output cells of the neural network, in turn, the classification of the states of the object’s elements is realized. Depending on the condition of the individual elements that make up the object, the method is able to indicate whether the elements are in working order, out of order or require quick repair/replacement.

Highlights

In the diagnostics of low-power solar plant devices, the proprietary intelligent DIAG 2 system operating on the basis of an artificial neural network of the RBF type was used
The descriptive part of the article presents the algorithm of the diagnostic method implemented in the DIAG 2 intelligent diagnostic system
The diagnostic method presented in the DIAG 2 system utilizes the method known in the literature [10,11] used for comparing diagnostic signals with the appropriate standard diagnostic signal vector assigned to them, cf

Summary

Introduction

Artificial intelligence systems including neural networks are extensively elaborated on in the literature, in the studies [1,2,3,4,5,6,7] These studies constitute a sufficient compendium of knowledge concerning the principle of the functioning of artificial neural networks. The authors described well the theoretical bases of the construction of static neural networks, and ways of teaching and training them. These studies can be useful when designing artificial neural networks that function based on radial base functions, including their structures, teaching and practice [4,5]. A significant part of these studies concerns the use of sets and fuzzy knowledge in the functioning of artificial neural networks [8,9,10,11]

Methods

Results

Discussion

Conclusion