Development of a Hybrid Expert Diagnostic System for Power Transformers Based on the Integration of Computational and Measurement Complexes
Ivan Beloev,
Mikhail Evgenievich Alpatov,
Marsel Sharifyanovich Garifullin,
Ilgiz Fanzilevich Galiev,
Shamil Faridovich Rakhmankulov,
Iliya Iliev and
Ylia Sergeevna Valeeva ()
Additional contact information
Ivan Beloev: Department of Transport, “Angel Kanchev” University of Ruse, 7017 Ruse, Bulgaria
Mikhail Evgenievich Alpatov: JSC “PK HC ELEKTROZAVOD”, 107023 Moscow, Russia
Marsel Sharifyanovich Garifullin: Department of Electric Power Systems and Networks, FGBOU VO “KGEU”, 420066 Kazan, Russia
Ilgiz Fanzilevich Galiev: Department of Electric Power Systems and Networks, FGBOU VO “KGEU”, 420066 Kazan, Russia
Shamil Faridovich Rakhmankulov: Department of Electric Power Systems and Networks, FGBOU VO “KGEU”, 420066 Kazan, Russia
Iliya Iliev: Department of Heat, Hydraulics and Environmental Engineering, “Angel Kanchev” University of Ruse, 7017 Ruse, Bulgaria
Ylia Sergeevna Valeeva: Department Project Activities, Russian University of Cooperation, 420034 Kazan, Russia
Energies, 2025, vol. 18, issue 20, 1-32
Abstract:
The paper presents a hybrid intelligent expert diagnostic system (HIESD) of power transformer (PT) subsystems realized on the basis of integration of measuring and computing hardware and software complexes into a single functional architecture. HIESD performs online diagnostics of four main subsystems of PT: 1—insulating (liquid and solid insulation); 2—electromagnetic (windings, magnetic conductor); 3—voltage regulation; and 4—high-voltage inputs. Computational complexes and modules of the system are connected with the real object of power grids, 110/10 kV substation, which interact with each other and contain a relational database of retrospective offline data of the PT “life cycle” (including test and measurement results), supplemented by online monitoring data of the main subsystems, corrected by high-precision test measurements; analytical complex, in which the work of calculation modules of the operational state of PT subsystems is supplemented by predictive analytics and machine learning modules; and a knowledge base, sections of which are regularly updated and supplemented. The system architecture is tested at industrial facilities in terms of online transformer diagnostics based on dissolved gas analysis (DGA) data. Additionally, a theoretical model of diagnostics based on the electromagnetic characteristics of the transformer, which takes into account distorted and nonlinear modes of its operation, is presented. The scientific significance of the work consists of the presentation of the following new provisions: Methodology and algorithm for diagnostics of electromagnetic parameters of ST, taking into account nonlinearity and non-sinusoidality of winding currents and voltages; formation of optimal client–service architecture of training models of hybrid system based on the processes of data storage and management; and modification of the moth–flame algorithm to optimize the smoothing coefficient in the process of training a probabilistic neural network
Keywords: power transformer; subsystems diagnostics; offline and online monitoring; hardware–software complex; relational database; expert system; artificial intelligence methods (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:20:p:5360-:d:1769196
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