scholarly journals Analisis Gas Terlarut pada Minyak Isolasi sebagai Indikator Kegagalan Transformator Daya dengan Metode Dissolved Gas Analysis

2019 ◽  
Vol 1 (2) ◽  
pp. 99-112
Author(s):  
Misto Misto ◽  
Haryono Haryono
Keyword(s):  
Gas Analysis ◽  
Dissolved Gas ◽  
Dissolved Gas Analysis ◽  
Combustible Gas ◽  
Fault Gas

Transformator daya merupakan peralatan utama dan yang paling penting dalam sistem penyaluran tenaga listrik. Sistem operasional pada transformator daya ini terdapat permasalahan yang umum terjadi seperti kegagalan thermal dan kegagalan elektris. Kegagalan thermal dan elektris umumnya menghasilkan fault gas. Minyak isolasi pada transformator daya selain sebagai pendingin juga berfungsi melarutkan gas-gas akibat kegagalan thermal dan kegagalan elektris. Informasi mengenai adanya indikasi kegagalan pada transformator dapat diperoleh dari hasil identifikasi jenis dan jumlah konsentrasi gas yang terlarut dalam minyak, atau biasa disebut dengan metode Dissolved Gas Analysis (DGA). Metode DGA dapat dilakukan dengan Total Dissolved Combustible Gas (TDCG), Key Gas, Roger’s Ratio, Ratio CO2/CO, dan Duval’s Triangle yang sesuai dengan IEEE std. C57-104.1991 dan IEC 60599. TDCG juga dapat digunakan untuk menentukan jadwal pengujian DGA. Berdasarkan  analisis yang  telah  dilakukan, Transformator Daya II Gardu Induk Tanggul  pada  tahun 2011,2012 dan 2013 dengan metode TDCG transformator dalam kondisi 2, Key Gas diperoleh CO sebagai gas kunci dengan indikasi kegagalan overheating cellulose,Ratio CO2/CO  menunjukkan proses  pemburukan sedang terjadi pada  isolasi kertas akibat kegagalan high thermal dengan temperature 200°C, Roger’s Ratio terjadi Thermal fault dengan temperature 150 - 300°C  dan Duval’s Triangle berada di luar kriteria evaluasi dan jadwal pengujian DGA selanjutnya adalah tiga bulanan.

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 Improved Fuzzy C-Means Clustering for Transformer Fault Diagnosis Using Dissolved Gas Analysis Data

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2344 ◽  
Author(s):  
Enwen Li ◽  
Linong Wang ◽  
Bin Song ◽  
Siliang Jian
Keyword(s):  
Fault Diagnosis ◽  
Membership Function ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Gas Analysis ◽  
Dissolved Gas ◽  
Fuzzy C Means ◽  
Dissolved Gas Analysis ◽  
Fcm Clustering ◽  
Transformer Fault

Dissolved gas analysis (DGA) of the oil allows transformer fault diagnosis and status monitoring. Fuzzy c-means (FCM) clustering is an effective pattern recognition method, but exhibits poor clustering accuracy for dissolved gas data and usually fails to subsequently correctly classify transformer faults. The existing feasible approach involves combination of the FCM clustering algorithm with other intelligent algorithms, such as neural networks and support vector machines. This method enables good classification; however, the algorithm complexity is greatly increased. In this paper, the FCM clustering algorithm itself is improved and clustering analysis of DGA data is realized. First, the non-monotonicity of the traditional clustering membership function with respect to the sample distance and its several local extrema are discussed, which mainly explain the poor classification accuracy of DGA data clustering. Then, an exponential form of the membership function is proposed to obtain monotony with respect to distance, thereby improving the dissolved gas data clustering. Likewise, a similarity function to determine the degree of membership is derived. Test results for large datasets show that the improved clustering algorithm can be successfully applied for DGA-data-based transformer fault detection.

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