Calculation of Health Index for Power Transformer Solid Insulation Using Fuzzy Logic

2021 ◽  
pp. 585-597
Author(s):  
Teruvai Manoj ◽  
Chilaka Ranga
Keyword(s):  
Fuzzy Logic ◽  
Power Transformer ◽  
Health Index ◽  
Solid Insulation

Fuzzy Logic Based Calculation and Analysis of Health Index for Power Transformer Installed in Grid Stations

2019 ◽  
Author(s):  
Muhammad Idrees ◽  
Muhammad Tanveer Riaz ◽  
Aashir Waleed ◽  
Zahir Javed Paracha ◽  
Hafiz Ahmad Raza ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Power Transformer ◽  
Health Index

scholarly journals Multiparameter-Based Fuzzy Logic Health Index Assessment for Oil-Immersed Power Transformers

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Edwell Tafara Mharakurwa ◽  
Rutendo Goboza
Keyword(s):  
Fuzzy Logic ◽  
Asset Management ◽  
Power Transformer ◽  
Carbon Oxide ◽  
Degree Of Polymerization ◽  
Health Index ◽  
Estimation Model ◽  
Inference System ◽  
Index Model ◽  
Power Utilities

The health index scheme can be the most fundamental tool that unifies all transformer condition status information into a singular outcome, thereby enhancing the power transformer asset management and life longevity strategies. This study aims at establishing a multiple parameter-dependent transformer health index estimation model cascaded with a fuzzy logic inference system. This strategy is centered on the effect of dynamic loading regime, varying hotspot temperatures and multiple attesting results of the insulation system. Furthermore, a nonintrusive degree of polymerization (DP) model based on furans and carbon oxide ratios as DP pointers is also factored in developing the health index model. The general outcome of the health index depends on entirely considered elements, but not on any isolated attribute. Data obtained from in-service transformers were used to validate the proposed model. The outcome of the model mirrors the practical condition of the evaluated transformers. Therefore, the proposed health index model can be a vital tool to asset managers and power utilities.

Inter-turn fault stability enrichment and diagnostic analysis of power system network using wavelet transformation-based sample data control and fuzzy logic controller

2021 ◽  
pp. 014233122110070
Author(s):  
Arunesh Kumar Singh ◽  
Abhinav Saxena ◽  
Nathuni Roy ◽  
Umakanta Choudhury
Keyword(s):  
Fuzzy Logic ◽  
Power System ◽  
Wavelet Transformation ◽  
Fuzzy Logic Controller ◽  
Power Transformer ◽  
Harmonic Distortion ◽  
Inrush Current ◽  
Data Control ◽  
Sample Data ◽  
Z Transformation

In this paper, performance analysis of power system network is carried out by injecting the inter-turn fault at the power transformer. The injection of inter-turn fault generates the inrush current in the network. The power system network consists of transformer, current transformer, potential transformer, circuit breaker, isolator, resistance, inductance, loads, and generating source. The fault detection and termination related to inrush current has some drawbacks and limitations such as slow convergence rate, less stability and more distortion with the existing methods. These drawbacks motivate the researchers to overcome the drawbacks with new proposed methods using wavelet transformation with sample data control and fuzzy logic controller. The wavelet transformation is used to diagnose the fault type but contribute lesser for fault termination; due to that, sample data of different signals are collected at different frequencies. Further, the analysis of collected sample data is assessed by using Z-transformation and fuzzy logic controller for fault termination. The stability, total harmonic distortion and convergence rate of collected sample data among all three methods (wavelet transformation, Z-transformation and fuzzy logic controller) are compared for fault termination by using linear regression analysis. The complete performance of fault diagnosis along with fault termination has been analyzed on Simulink. It is observed that after fault injection at power transformer, fault recovers faster under fuzzy logic controller in comparison with Z-transformation followed by wavelet transformation due to higher stability, less total harmonic distortion and faster convergence.

An approach to power transformer asset management using health index

2009 ◽  
Vol 25 (2) ◽  
pp. 20-34 ◽  
Author(s):  
A. Jahromi ◽  
R. Piercy ◽  
S. Cress ◽  
J. Service ◽  
Wang Fan
Keyword(s):  
Asset Management ◽  
Power Transformer ◽  
Health Index

Oil health index calculation and incipient fault diagnosis in power transformers using fuzzy logic

2022 ◽  
Vol 64 (1) ◽  
pp. 28-37
Author(s):  
T Manoj ◽  
C Ranga
Keyword(s):  
Fuzzy Logic ◽  
Logic Model ◽  
Transformer Oil ◽  
Power Transformers ◽  
Dissolved Gases ◽  
Health Index ◽  
Fuzzy Logic Model ◽  
Proposed Model ◽  
Incipient Faults ◽  
Incipient Fault

In this paper, a new fuzzy logic (FL) model is proposed for assessing the health status of power transformers. In addition, the detection of incipient faults is achieved where two or more faults exist simultaneously. The process is carried out by integrating a fuzzy logic model with the conventional International Electric Committee (IEC) ratio codes method. As transformer oil insulation deteriorates, excess percentages of dissolved gases such as hydrogen, methane, ethane, acetylene and ethylene are induced within the trasnformer. The status of oil health is generally assessed using these gas concentrations. Therefore, in the proposed model, 31 fuzzy rules are designed based on the severity levels of these gases in order to determine the health index (HI) of the oil. Similarly, any incipient faults along with their severity are also detected using the proposed fuzzy logic model with 22 expert rules. To validate the proposed fuzzy logic model, the data for dissolved gases in 50 working transformers operated by the Himachal Pradesh State Electricity Board (HPSEB), India, are collected. Over the years, calculations for the health index have been performed using conventional dissolved gas analysis (DGA) interpretation methods. The shortcomings of these methods, such as non-reliability and inaccuracy, are successfully overcome using the proposed model. The detection of incipient faults is normally performed using key gas, Rogers ratios, the Duval triangle, Dornenburg ratios, modified Rogers ratios and the IEC ratio codes methods. The shortcomings of these conventional ratio code methods in identifying incipient faults in some typical cases, ie multiple incipient fault cases, are overcome by the proposed fuzzy logic model.

A Fuzzy Logic Proposal for Diagnosis Multiple Incipient Faults in a Power Transformer

2021 ◽  
pp. 187-198
Author(s):  
J. C. Fernández ◽  
L. B. Corrales ◽  
F. H. Hernández ◽  
I. F. Benítez ◽  
J. R. Núñez
Keyword(s):  
Fuzzy Logic ◽  
Power Transformer ◽  
Incipient Faults

scholarly journals A Fuzzy Logic Model for Power Transformer Faults’ Severity Determination Based on Gas Level, Gas Rate, and Dissolved Gas Analysis Interpretation

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 1009 ◽  
Author(s):  
Rahman Azis Prasojo ◽  
Harry Gumilang ◽  
Suwarno ◽  
Nur Ulfa Maulidevi ◽  
Bambang Anggoro Soedjarno
Keyword(s):  
Fuzzy Logic ◽  
Power Transformer ◽  
Local Population ◽  
Gas Analysis ◽  
Power Transformers ◽  
Support Vector ◽  
Dissolved Gas ◽  
New Approach ◽  
Dissolved Gas Analysis ◽  
Novel Approach

In determining the severity of power transformer faults, several approaches have been previously proposed; however, most published studies do not accommodate gas level, gas rate, and Dissolved Gas Analysis (DGA) interpretation in a single approach. To increase the reliability of the faults’ severity assessment of power transformers, a novel approach in the form of fuzzy logic has been proposed as a new solution to determine faults’ severity using the combination of gas level, gas rate, and DGA interpretation from the Duval Pentagon Method (DPM). A four-level typical concentration and rate were established based on the local population. To simplify the assessment of hundreds of power transformer data, a Support Vector Machine (SVM)-based DPM with high agreements to the graphical DPM has been developed. The proposed approach has been implemented to 448 power transformers and further implementation was done to evaluate faults’ severity of power transformers from historical DGA data. This new approach yields in high agreement with the previous methods, but with better sensitivity due to the incorporation of gas level, gas rate, and DGA interpretation results in one approach.

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