The issue of an effective assessment of the technical condition of the mechanical structure of the power transformer core and windings originated the research work discussed in this paper. This publication presents the results which indicate the possibility of developing a new and noninvasive diagnostic method of the transformer active part. The method developed is based on the time-frequency analysis results of the mechanical vibrations measured of the monitored appliance. The registration of vibrations is performed during switching the transformer monitored into idle operation. This paper characterizes the power object under study, the measuring system applied, and discusses a new methodology of assessment of the technical condition of the core and windings using a modified vibroacoustic method. The original measurement results presented in this paper were obtained during switching on a 200-kVA transformer in laboratory conditions. The analysis of the signals measured was carried out for four operating conditions of the transformer under study: 1) with the core pressed by the manufacturer; 2) with the core with loose screws fixing the upper yoke; 3) with the core with loose screws fixing the upper and lower yokes; and 4) with the core with loose screws fixing the upper and lower yokes with a simultaneous unpressing of the windings of the upper voltage side.

The genesis of the research work presented in this paper constitutes the issue of the effective and efficient recognition of single-source one-time partial discharge forms that can occur in insulation systems of power transformers. The paper presents research results referring to the use of single-direction artificial neural networks for recognizing basic partial discharge forms that can occur in paper-oil insulation impaired by aging processes. The research work results presented show the recognition effectiveness of basic partial discharge forms depending on the descriptor of the analysis of the acoustic emission signal analysis. The detailed cognitive aim was selection of input parameters and an artificial neural network which would be the best, considering recognition effectiveness and processing time, and which could be used as a classifier in an expert diagnostic system making identification of partial discharges measured by using the acoustic method possible.

Aiming at solving the problem of dual resource constrained flexible job shop scheduling problem (DRCFJSP) with differences in operating time between operators, an artificial intelligence (AI)-based DRCFJSP optimization model is developed in this paper. This model introduces the differences between the loading and unloading operation time of workers before and after the process. Subsequently, the quantum genetic algorithm (QGA) is used as the carrier; the process is coded through quantum coding; and the niche technology is used to initialize the population, adaptive rotation angle, and quantum mutation strategy to improve the efficiency of the QGA and avoid premature convergence. Lastly, through the Kacem standard calculation example and the reliability analysis of the factory workshop processing process example, performance evaluation is conducted to show that the improved QGA has good convergence and does not fall into premature ability, the improved QGA can solve the problem of reasonable deployment of machines and personnel in the workshop, and the proposed method is more effective for the DRCFJSP than some existing methods. The findings can provide a good theoretical basis for actual production and application.

The subject matter of this paper refers to the improvement of the acoustic emission (AE) method when used for detection, measurement and location of partial discharges (PDs) in oil insulation systems of power appliances. In particular, presents the basic assumptions and describe the individual elements of an expert diagnostic system, which uses an acoustic method to asset the state of the measured power transformer insulation, performed during their normal work in industrial conditions. The system will consist of four basic modules, i.e. measuring system, processing-analyzing system, knowledge base and classifier. These modules are characterized successively in Point 2 of the paper. Special attention is given to the description of the group of multiparametric descriptors characterizing the AE signals in the time, frequency and time-frequency domains, to descriptive statistics indexes and correlative parameters. Accordant descriptors make it possible, at strictly defined metrological conditions, to recognize basic PD forms that may occur in paper-oil insulation. In this way a catalogued knowledge base containing the socalled 'fingerprints' was created for basic types of high-voltage defects of insulation system.

This study presents the case study of a substation in‐service power transformer referred to the on‐site partial discharge (PD) detection and evaluation methods. An original methodology for simultaneous application of three methods is proposed: electrical, acoustic and ultra‐high frequency. Transformer is powered by the power grid and no external generator is required according to the proposed methodology. Furthermore, several possibilities of applying these concurrent measurements and benefits of such solution in terms of result interpretation, interference resistance and on‐site measurement applicability are indicated. The proposed methodology allows for a fast, accurate and secure PD diagnostics in a power transformer under on‐site operating conditions. In the described example, a failure as a surface PD in the vicinity of the first phase of high voltage winding output is detected. The research results provide the basis for further works on a diagnostic expert system, which exploits automatic detection and evaluation of the PD phenomenon in power transformers under normal operating conditions.

This paper provides an example of the application of the acoustic emission (AE) method for the diagnosis of technical conditions of a three-phase on-load tap-changer (OLTC) GIII type. The measurements were performed for an amount of 10 items of OLTCs, installed in power transformers with a capacity of 250 MVA. The study was conducted in two different OLTC operating conditions during the tapping process: under load and free running conditions. The analysis of the measurement results was made in both time domain and time-frequency domain. The description of the AE signals generated by the OLTC in the time domain was performed using the analysis of waveforms and determined characteristic times. Within the time-frequency domain the measured signals were described by short-time Fourier transform spectrograms.