Diagnostics of low-capacity solar power station equipment with 2- and 3-valued logic

Abstract

The paper outlines research issues relating to 2- and 3-valued logic diagnoses developed with the diagnostic system (DIA G 2) for the equipment installed at a low-capacity solar power station. The presentation is facilitated with an overview and technical description of the functional and diagnostic model of the low-power solar power station. A model of the low-power solar power station (the tested facility, a.k.a. the test object) was developed, from which a set of basic elements and a set of diagnostic outputs were determined and developed by the number of functional elements j of j. The work also provides a short description of the smart diagnostic system (DIA G 2) used for the tests shown herein. (DIA G 2) is a proprietary work. The diagnostic program of (DIA G 2) operates by comparing a set of actual diagnostic output vectors to their master vectors. The output of the comparison are elementary divergence metrics of the diagnostic output vectors determined by a neural network. The elementary divergence metrics include differential distance metrics which serve as the inputs for (DIA G 2) to deduct the state (condition) of the basic elements of the tested facility. Keywords: technical diagnostics, diagnostic inference, multiple-valued logic, artificial intelligence.