A Comparison between Artificial Intelligence Method and Standard Diagnosis Methods for Power Transformer Dissolved Gas Analysis Using Two Public Databases

2020 ◽  
Vol 15 (9) ◽  
pp. 1305-1311
Author(s):  
Hongjie Zheng ◽  
Ryuji Shioya
Keyword(s):  
Artificial Intelligence ◽  
Power Transformer ◽  
Gas Analysis ◽  
Dissolved Gas ◽  
Dissolved Gas Analysis

Power Transformer Fault Diagnosis using DGA and Artificial Intelligence

2020 ◽  
Vol 13 (4) ◽  
pp. 579-587
Author(s):  
Seyed Javad Tabatabaei Shahrabad ◽  
Vahid Ghods ◽  
Mohammad Tolou Askari
Keyword(s):  
Artificial Intelligence ◽  
Decision Tree ◽  
Power Transformer ◽  
Gas Analysis ◽  
Power Transformers ◽  
Detection Methods ◽  
Support Vector ◽  
Dissolved Gas ◽  
Dissolved Gas Analysis ◽  
Internal Faults

Background: Power transformers are one of the most applicable electricity network devices which transmit output power of the generator to the network through increasing voltage and decreasing current. Due to high cost of such devices and cost of disconnecting device upon failure, disconnection and failure of the transformer should be avoided as much as possible. Objective: In addition, in order to increase reliability and reduce maintenance costs, such devices should be monitored constantly. Internal faults ionize and warm up oil and as a result, gases like carbon dioxide, methane, ethane, ethylene and acetylene are produced. Various methods have been proposed for diagnosing fault in power transformers where one of the most well-known methods is dissolved gas analysis (DGA). DGA in oil is one of the effective tools for diagnosing initial faults in transformers. Methods: Common fault detection methods using oil-dissolved gas analysis include Dornemburge, Duval’s triangle, IEC/IEEE standard, key gases and Rogers. In recent years, artificial intelligence like genetic algorithm, fuzzy logic and neural networks have been used to detect faults using DGA. In this paper, support vector machine (SVM) and decision tree are used to detect internal faults in power transformers. Results: By evaluation of the proposed methods, total accuracies of classifiers using SVM and decision tree were 90% and 97.5%, respectively. Conclusion: Decision tree shows better performance and it is suggested as a proper method for obtaining promising results.

scholarly journals Towards Precise Interpretation of Oil Transformers via Novel Combined Techniques Based on DGA and Partial Discharge Sensors

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2223 ◽  
Author(s):  
Sayed A. Ward ◽  
Adel El-Faraskoury ◽  
Mohamed Badawi ◽  
Shimaa A. Ibrahim ◽  
Karar Mahmoud ◽  
...  
Keyword(s):  
Power Transformer ◽  
Partial Discharge ◽  
Electrical Power ◽  
Diagnostic Techniques ◽  
Gas Analysis ◽  
Economic Losses ◽  
Dissolved Gas ◽  
Electricity Transmission ◽  
Dissolved Gas Analysis ◽  
Precise Interpretation

Power transformers are considered important and expensive items in electrical power networks. In this regard, the early discovery of potential faults in transformers considering datasets collected from diverse sensors can guarantee the continuous operation of electrical systems. Indeed, the discontinuity of these transformers is expensive and can lead to excessive economic losses for the power utilities. Dissolved gas analysis (DGA), as well as partial discharge (PD) tests considering different intelligent sensors for the measurement process, are used as diagnostic techniques for detecting the oil insulation level. This paper includes two parts; the first part is about the integration among the diagnosis results of recognized dissolved gas analysis techniques, in this part, the proposed techniques are classified into four techniques. The integration between the different DGA techniques not only improves the oil fault condition monitoring but also overcomes the individual weakness, and this positive feature is proved by using 532 samples from the Egyptian Electricity Transmission Company (EETC). The second part overview the experimental setup for (66/11.86 kV–40 MVA) power transformer which exists in the Egyptian Electricity Transmission Company (EETC), the first section in this part analyzes the dissolved gases concentricity for many samples, and the second section illustrates the measurement of PD particularly in this case study. The results demonstrate that precise interpretation of oil transformers can be provided to system operators, thanks to the combination of the most appropriate techniques.

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 Power Transformer Fault Diagnosis Based on Dissolved Gas Analysis by Correlation Coefficient-DBSCAN

2020 ◽  
Vol 10 (13) ◽  
pp. 4440 ◽  
Author(s):  
Yongxin Liu ◽  
Bin Song ◽  
Linong Wang ◽  
Jiachen Gao ◽  
Rihong Xu
Keyword(s):  
Fault Diagnosis ◽  
Correlation Coefficient ◽  
Power Transformer ◽  
Gas Analysis ◽  
Gas Content ◽  
Amplification Coefficient ◽  
Dissolved Gas ◽  
Dissolved Gas Analysis ◽  
Density Clustering ◽  
Transformer Fault

The transformers work in a complex environment, which makes them prone to failure. Dissolved gas analysis (DGA) is one of the most important methods for oil-immersed transformers’ internal insulation fault diagnosis. In view of the high correlation of the same fault data of transformers, this paper proposes a new method for transformers’ fault diagnosis based on correlation coefficient density clustering, which uses density clustering to extrapolate the correlation coefficient of DGA data. Firstly, we calculated the correlation coefficient of dissolved gas content in the fault transformers oil and enlarged the correlation of the same fault category by introducing the amplification coefficient, and finally we used the density clustering method to cluster diagnosis. The experimental results show that the accuracy of clustering is improved by 32.7% compared with the direct clustering judgment without using correlation coefficient, which can effectively cluster different types of transformers fault modes. This method provides a new idea for transformers fault identification, and has practical application value.

Sign in / Sign up

Export Citation Format

Share Document