Laser Control and Measuring Complex for Non-contact Vibration Control of the Power Transformer Technical Condition

2021 ◽  
pp. 157-167
Author(s):  
Vasily Basenko ◽  
Oleg Vladimirov ◽  
Igor Ivshin ◽  
Marat Nizamiev ◽  
Ilnur Usmanov
Keyword(s):  
Vibration Control ◽  
Power Transformer ◽  
Technical Condition ◽  
Measuring Complex ◽  
Laser Control

scholarly journals Contactless laser control and measuring complex for determining the level of winding pressure and magnetic conductor of power transformer

2021 ◽  
Vol 23 (3) ◽  
pp. 155-168
Author(s):  
V. R. Basenko ◽  
O. V. Vladimirov ◽  
I. V. Ivshin ◽  
M. F. Nizamiev
Keyword(s):  
Magnetic Circuit ◽  
Power Transformer ◽  
Technical Condition ◽  
Laser Vibrometer ◽  
Magnetic Conductor ◽  
Labview Software ◽  
Measuring Complex ◽  
Amplitude Spectra ◽  
Laser Control ◽  
Vibration Parameters

TARGET. The purpose of this work is to develop a non-contact laser control and measuring complex (LCMC) for vibration control of the level of pressing of windings and magnetic circuit of a power transformer. A laser vibrometer is used as a measuring element, the developed software in the LabVIEW graphical programming environment serves as a tool for processing vibration signals. The technical condition of the power transformer is analyzed by the amplitude spectra of the vibration of the tank of the transformer under study, formed using the fast Fourier transform algorithm in the LabVIEW software.METHODS. The vibration control method makes it possible to control a power transformer during its operation under voltage, which makes it possible to move from a planned system of transformer repairs to a system for taking out for repair according to the current technical condition.RESULTS. The developed LCMC allows to carry out non-contact measurements of vibration parameters of a power transformer under voltage and to establish the current level of pressing of windings and magnetic circuit.CONCLUSION. The developed LCMC with software allows contactless control of the technical condition of the magnetic circuit and the windings of the power transformer according to the amplitude-frequency characteristics of the vibration parameters, as well as the use of statistical methods for processing and analyzing signals received from the transformer.

scholarly journals Contactless laser control and measuring complex for determining the level of winding pressure and magnetic conductor of power transformer

2021 ◽  
Vol 23 (3) ◽  
pp. 140-153
Author(s):  
V. R. Basenko ◽  
O. V. Vladimirov ◽  
I. V. Ivshin ◽  
M. F. Nizamiev
Keyword(s):  
Magnetic Circuit ◽  
Power Transformer ◽  
Technical Condition ◽  
Laser Vibrometer ◽  
Magnetic Conductor ◽  
Labview Software ◽  
Measuring Complex ◽  
Amplitude Spectra ◽  
Laser Control ◽  
Vibration Parameters

TARGET. The purpose of this work is to develop a non-contact laser control and measuring complex (LCMC) for vibration control of the level of pressing of windings and magnetic circuit of a power transformer. A laser vibrometer is used as a measuring element, the developed software in the LabVIEW graphical programming environment serves as a tool for processing vibration signals. The technical condition of the power transformer is analyzed by the amplitude spectra of the vibration of the tank of the transformer under study, formed using the fast Fourier transform algorithm in the LabVIEW software.METHODS. The vibration control method makes it possible to control a power transformer during its operation under voltage, which makes it possible to move from a planned system of transformer repairs to a system for taking out for repair according to the current technical condition.RESULTS. The developed LCMC allows to carry out non-contact measurements of vibration parameters of a power transformer under voltage and to establish the current level of pressing of windings and magnetic circuit.CONCLUSION. The developed LCMC with software allows contactless control of the technical condition of the magnetic circuit and the windings of the power transformer according to the amplitude-frequency characteristics of the vibration parameters, as well as the use of statistical methods for processing and analyzing signals received from the transformer.

scholarly journals Forecast model for estimating the service life of a diagnosed object based on the Neyman–Pearson method

2018 ◽  
Vol 216 ◽  
pp. 03011
Author(s):  
Sergey Barsukov ◽  
Sergey Pakhomov
Keyword(s):  
Service Life ◽  
Power Transformer ◽  
Impurity Content ◽  
Forecast Model ◽  
Threshold Value ◽  
Technical Condition ◽  
Transformer Oil ◽  
Average Risk ◽  
Iron Impurity ◽  
Object Based

The paper is aimed at developing a forecast model for estimating the service life of a diagnosed object based on the Neyman–Pearson method. It presents a procedure for selecting necessary and sufficient number of diagnostic indicators using the forecast model. The technique has been tested on the basis of a power transformer with a liquid dielectric. A condition-based operation strategy has been proposed for the transformer. According to this strategy, the iron impurity content in the dielectric liquid (oil) of the transformer should be measured every year of operation. Based on the forecast model, it is possible to calculate the variation of average risk (R) and a threshold value of iron impurity content in the transformer oil (k0) for each year of operation. Using these parameters, a reliable forecast model can be constructed to estimate the remaining service life of the transformer. The obtained relationships make it possible to identify a scientifically grounded stage in the service life of a diagnosed object, at which the number of measurable diagnostic indicators (indicators that are necessary for assessing the real technical condition of equipment) can be minimized.

scholarly journals Assessment of the current technical condition of electrical equipment based on an artificial neural network

2021 ◽  
Vol 2131 (5) ◽  
pp. 052049
Author(s):  
V Z Manusov ◽  
M R Otuzbaev ◽  
M A Scherbinina ◽  
G V Ivanov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Efficiency ◽  
Power Transformer ◽  
Reliability And Validity ◽  
Electrical Equipment ◽  
Technical Condition ◽  
Computer Assisted ◽  
Technical Diagnostics ◽  
Artificial Neural

Abstract Assessment of the current technical condition is an important task, so the state of electrical equipment depends on its further operability. Thanks to modern computing devices, it is possible to implement actively artificial intelligence and computer-assisted learning methods that allow achieving high efficiency in data processing. A study was conducted and an algorithm for diagnosing the technical condition based on an artificial neural network was developed. A model based on a multilayer perceptron is proposed, which allows evaluating the technical condition of a high-voltage power transformer. The result of the technical diagnostics of the model is the assignment of the condition to one of the five classes, proposed by the guidelines presented by the International Council on Large Electrical Systems. The methodology is presented on the example of a 250 MVA transformer with a certain defect history, which allowed us to show the reliability and validity of the obtained results. It is shown that the use of the proposed model makes it possible to achieve accuracy in determining the technical condition of 0.95. The introduction of this model into an automated monitoring and diagnostics system will allow assessing the technical condition of electrical equipment in real time with sufficient accuracy.

scholarly journals Risk Assessment Algorithm for Power Transformer Fleets Based on Condition and Strategic Importance

Algorithms ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 319
Author(s):  
Diego A. Zaldivar ◽  
Andres A. Romero ◽  
Sergio R. Rivera
Keyword(s):  
Power Transformer ◽  
Critical Role ◽  
Technical Condition ◽  
Electric Power System ◽  
Assessment Model ◽  
Fuzzy Logic Algorithm ◽  
Inherent Risk ◽  
Importance Index ◽  
Utmost Care ◽  
System Power

In every electric power system, power transformers (PT) play a critical role. Under ideal circumstances, PT should receive the utmost care to maintain the highest operative condition during their lifetime. Through the years, different approaches have been developed to assess the condition and the inherent risk during the operation of PT. However, most proposed methodologies tend to analyze PT as individuals and not as a fleet. A fleet assessment helps the asset manager make sound decisions regarding the maintenance scheduling for groups of PT with similar conditions. This paper proposes a new methodology to assess the risk of PT fleets, considering the technical condition and the strategic importance of the units. First, the state of the units was evaluated using a health index (HI) with a fuzzy logic algorithm. Then, the strategic importance of each unit was assessed using a weighting technique to obtain the importance index (II). Finally, the analyzed units with similar HI and II were arranged into a set of clusters using the k-means clustering technique. A fleet of 19 PTs was used to validate the proposed method. The obtained results are also provided to demonstrate the viability and feasibility of the assessment model.

Development of Operation and Diagnostic Algorithm Complex for Technical Condition Assessment of Power Transformers in Permissible Risk Circumstances

2021 ◽  
Author(s):  
◽  
Gints Poišs
Keyword(s):  
Power Transformer ◽  
Diagnostic Algorithm ◽  
Condition Index ◽  
Technical Condition ◽  
Transmission System ◽  
Diagnostic Methods ◽  
Data Availability ◽  
Power Transformers ◽  
Risk Level ◽  
Data Set

A power transformer is a key unit in the transmission system, and its cut-off can impact both consumers and the general stability of the system. Therefore, it is an important tool for processing the operational and technical condition data to quantify them as the technical condition index (TCI). Based on the technical condition of the power transformer, the TCI enables objective and reasoned decisions on the future investments related to replacement or repairs of transformers. Thus, by using the TCI, the service life of the transformer can be safely extended, since the identified risks have been recognized and are being followed-up. The TCI method is useful for a power transformer park, because it allows easy identification of transformers that require most attention. A crucial precondition for this method is data availability, diversity, and regularity or frequency of data collection. These features (preconditions) may vary in different power transmission systems, and it creates the necessity for a tailored approach. The present Doctoral Thesis studies the diagnostic methods used in the transmission system in Latvia and the results thereof. Thus, it takes advantage of an already existing data set and flow to develop a TCI-based complex of algorithms for determining the risk level of high-power transformers in acceptable risk conditions.

scholarly journals Development of a fuzzy expert system for power transformer diagnostics

2021 ◽  
Vol 2091 (1) ◽  
pp. 012064
Author(s):  
A P Khlebtsov ◽  
A N Shilin ◽  
A V Rybakov ◽  
A Yu Klyucharev
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Power Transformer ◽  
Early Stage ◽  
Three Dimensional ◽  
Electrical Equipment ◽  
Technical Condition ◽  
Logic Device ◽  
Operational Information ◽  
R Programming

Abstract In this paper, an expert information system for assessing the technical condition of a power transformer is developed. The system will work on the basis of the fuzzy logic device, and provide operational information about the state of the power transformer. The paper uses fuzzy inference algorithms. The R programming language is used to write a program that uses fuzzy logic. We analyzed the data of chromatographic analysis of gases dissolved in oil, as well as the data of thermal imaging images, identifying the most heated points in power transformers. A database of fuzzy logic rules has been formed. Several examples of defuzzification of the results obtained by the center of gravity method are given. As a result of the program, a three-dimensional graph was obtained that characterizes the surface of the fuzzy output. The developed software package allows you to detect defects in working electrical equipment at an early stage of their development, which not only prevents a sudden shutdown of production as a result of an accident, but also significantly reduces the cost of repairing equipment and increases its service life

scholarly journals The Use of Generalized Gaussian Distribution in Vibroacoustic Detection of Power Transformer Core Damage

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2525
Author(s):  
Robert Krupiński ◽  
Eugeniusz Kornatowski
Keyword(s):  
Gaussian Distribution ◽  
Power Transformer ◽  
Vibration Signal ◽  
Technical Condition ◽  
Power Transformers ◽  
Generalized Gaussian Distribution ◽  
Core Damage ◽  
Vibroacoustic Diagnostics ◽  
Detector Output

Vibroacoustic diagnostics (VM—Vibroacoustic Method) is one of the methods for diagnosing the active part of power transformers. Measurement technologies have been refined over the past several years, but the methods of analyzing data obtained in VM diagnostics are still in development. In most cases, they are based on a simple frequency spectrum analysis, and the diagnostic conclusions are subjective and depend on the expert’s professional experience. The article presents an objective method for the detection of transformer unit core damage, based on the analysis of the statistical properties of the vibration signal registered on the surface of the tank of an unloaded transformer in the steady state of vibrations (VM). The algorithm for proceeding further is: FFT analysis of the vibroacoustic signal, with the determination of the relative changes in vibration power as a function of frequency P r ( f ) and, finally, the determination of the statistic properties of the dataset P r ( f ) . The Generalized Gaussian Distribution (GGD) is used to describe the P r ( f ) set. The detector output values are the λ and p parameters of the GGD distribution. These two numerical values form the basis for the classification of the technical condition of the transformer unit core. The correctness of the described solution was verified on the example of ten pieces of 16 MVA power transformers with different operating times and degrees of wear.

scholarly journals DIAGNOSIS OF INJECTORS OF HIGH-SPEED MARINE DIESEL ENGINES

2017 ◽  
pp. 50-58
Author(s):  
Konstantin Olegovich Sergeev ◽  
Andrew Adol'fovich Pankratov
Keyword(s):  
Vibration Control ◽  
Diesel Engines ◽  
High Speed ◽  
Vibration Signal ◽  
Technical Condition ◽  
Center Frequency ◽  
High Speed Diesel ◽  
Marine Diesel

The method of determining the technical condition of the injector of high-speed diesel using the method of vibration control on the injector body is considered. Vibration signal is measured by the accelerometer and is filtered by 1/3 - octave filter with a center frequency of 63 kHz. The measurements were performed at idle, 20%, 33%, 66% and 100% of rated power, as well as on the nominal power with an artificially introduced defect of the injector - spring breakage. The temporal realization of the signal is used for diagnosis. The conclusions about the nature of the change in the temporal realization of vibration signal depending on the power state and the presence of faults have been made. The possibility of determining of the cyclic supply and cylinder output using the vibration signal was estimated. The direction of further research to improve the accuracy of diagnosis has been proposed.

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