scholarly journals Improved Consistent Interpretation Approach of Fault Type within Power Transformers Using Dissolved Gas Analysis and Gene Expression Programming

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 730 ◽  
Author(s):  
Ahmed Abu-Siada
Keyword(s):  
Gene Expression ◽  
Expert System ◽  
Gene Expression Programming ◽  
Gas Analysis ◽  
Transformer Oil ◽  
Power Transformers ◽  
Dissolved Gas ◽  
Dissolved Gas Analysis ◽  
Fault Type ◽  
Industry Practice

Dissolved gas analysis (DGA) of transformer oil is considered to be the utmost reliable condition monitoring technique currently used to detect incipient faults within power transformers. While the measurement accuracy has become relatively high since the development of various off-line and on-line measuring sensors, interpretation techniques of DGA results still depend on the level of personnel expertise more than analytical formulation. Therefore, various interpretation techniques may lead to different conclusions for the same oil sample. Moreover, ratio-based interpretation techniques may fail in interpreting DGA data in case of multiple fault conditions and when the oil sample comprises insignificant amount of the gases used in the specified ratios. This paper introduces an improved approach to overcome the limitations of conventional DGA interpretation techniques, automate and standardize the DGA interpretation process. The approach is built based on incorporating all conventional DGA interpretation techniques in one expert system to identify the fault type in a more consistent and reliable way. Gene Expression Programming is employed to establish this expert system. Results show that the proposed approach provides more reliable results than using individual conventional methods that are currently adopted by industry practice worldwide.

scholarly journals Study of approaches to incipient fault detection in power transformer by using dissolved gas analysis

Energetika ◽  
2017 ◽  
Vol 63 (2) ◽  
Author(s):  
Ruta Liepniece ◽  
Sandra Vitolina ◽  
Janis Marks
Keyword(s):  
Fault Detection ◽  
Power Transmission ◽  
Power Transformer ◽  
Good Condition ◽  
Gas Analysis ◽  
Transformer Oil ◽  
Power Transformers ◽  
Dissolved Gas ◽  
Dissolved Gas Analysis ◽  
Incipient Fault

To maintain the reliability of power transmission it is important to detect the incipient fault of power transformer as early as possible. If the fault of a power transformer is not detected promptly, it can evolve resulting in high repair costs or even failure of the power transformer and decreasing reliability of power transmission. The most commonly used method for power transformer fault detection is the dissolved gas analysis (DGA) of transformer oil. Various methods have been developed to interpret the data of dissolved gas analysis, but not many are applicable for the detection of the incipient fault. The detection of the incipient fault of a power transformer is included in both IEEE C57.104-2008 “Guide for the Interpretation of Gases Generated in Oil-Immersed Transformers” and Standard of Latvian Electrotechnical Committee LEK 118 “Transformer Oil Inspection Standards”. In both standards, the limits of dissolved gases in transformer oil are divided into levels, each corresponding to different technical conditions of the power transformer including the level that indicates the incipient fault. However, these approaches vary to a great degree – one approach mostly indicates that transformers are in good condition with several cases that must be additionally evaluated, but the second approach mostly results in warning about the incipient fault, which must be confirmed by additional evaluation. The objective of this paper is to determine the most suitable approach to detect the incipient fault of power transformers. A case study is provided, which includes analysis of DGA data of 48 power transformers installed in the transmission network in Latvia with both methodologies mentioned above for detecting the incipient fault.

scholarly journals Dissolved Gas Analysis Equipment for Online Monitoring of Transformer Oil: A Review

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4057 ◽  
Author(s):  
Sergio Bustamante ◽  
Mario Manana ◽  
Alberto Arroyo ◽  
Pablo Castro ◽  
Alberto Laso ◽  
...  
Keyword(s):  
Electric Power ◽  
Online Monitoring ◽  
Oil Quality ◽  
Gas Analysis ◽  
Transformer Oil ◽  
Power Transformers ◽  
Quality Analysis ◽  
Dissolved Gas ◽  
Correct Operation ◽  
Dissolved Gas Analysis

Power transformers are the most important assets of electric power substations. The reliability in the operation of electric power transmission and distribution is due to the correct operation and maintenance of power transformers. The parameters that are most used to assess the health status of power transformers are dissolved gas analysis (DGA), oil quality analysis (OQA) and content of furfuraldehydes (FFA) in oil. The parameter that currently allows for simple online monitoring in an energized transformer is the DGA. Although most of the DGA continues to be done in the laboratory, the trend is online DGA monitoring, since it allows for detection or diagnosis of the faults throughout the life of the power transformers. This study presents a review of the main DGA monitors, single- or multi-gas, their most important specifications, accuracy, repeatability and measurement range, the types of installation, valve or closed loop, and number of analogue inputs and outputs. This review shows the differences between the main existing DGA monitors and aims to help in the selection of the most suitable DGA monitoring approach according to the needs of each case.

scholarly journals A Novel Fault Diagnosis Method for Power Transformer Based on Dissolved Gas Analysis Using Hypersphere Multiclass Support Vector Machine and Improved D–S Evidence Theory

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 4017 ◽  
Author(s):  
Haikun Shang ◽  
Junyan Xu ◽  
Zitao Zheng ◽  
Bing Qi ◽  
Liwei Zhang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Evidence Theory ◽  
Gas Analysis ◽  
Power Transformers ◽  
Support Vector ◽  
Dissolved Gas ◽  
Dissolved Gas Analysis ◽  
Diagnosis Method ◽  
Multiclass Support Vector Machine

Power transformers are important equipment in power systems and their reliability directly concerns the safety of power networks. Dissolved gas analysis (DGA) has shown great potential for detecting the incipient fault of oil-filled power transformers. In order to solve the misdiagnosis problems of traditional fault diagnosis approaches, a novel fault diagnosis method based on hypersphere multiclass support vector machine (HMSVM) and Dempster–Shafer (D–S) Evidence Theory (DET) is proposed. Firstly, proper gas dissolved in oil is selected as the fault characteristic of power transformers. Secondly, HMSVM is employed to diagnose transformer fault with selected characteristics. Then, particle swarm optimization (PSO) is utilized for parameter optimization. Finally, DET is introduced to fuse three different fault diagnosis methods together, including HMSVM, hybrid immune algorithm (HIA), and kernel extreme learning machine (KELM). To avoid the high conflict between different evidences, in this paper, a weight coefficient is introduced for the correction of fusion results. Results indicate that the fault diagnosis based on HMSVM has the highest probability to identify transformer faults among three artificial intelligent approaches. In addition, the improved D–S evidence theory (IDET) combines the advantages of each diagnosis method and promotes fault diagnosis accuracy.

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.

scholarly journals Dissolved Gas Analysis in Transformer Oil Using Pt-Doped WSe2 Monolayer Based on First Principles Method

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 72012-72019 ◽  
Author(s):  
Zhenwei Chen ◽  
Xiaoxing Zhang ◽  
Hao Xiong ◽  
Dachang Chen ◽  
Hongtu Cheng ◽  
...  
Keyword(s):  
First Principles ◽  
Gas Analysis ◽  
Transformer Oil ◽  
Dissolved Gas ◽  
Dissolved Gas Analysis ◽  
First Principles Method
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