Impact of Load Ramping on Power Transformer Dissolved Gas Analysis

Dissolved gas in oil analysis (DGA) is one of the most reliable condition monitoring techniques, which is currently used by the industry to detect incipient faults within the power transformers. While the technique is well matured since the development of various offline and online measurement techniques along with various interpretation methods, no much attention was given so far to the oil sampling time and its correlation with the transformer loading. A power transformer loading is subject to continuous daily and seasonal variations, which is expected to increase with the increased penetration level of renewable energy sources of intermittent characteristics, such as photovoltaic (PV) and wind energy into the current electricity grids. Generating unit transformers also undergoes similar loading variations to follow the demand, particularly in the new electricity market. As such, the insulation system within the power transformers is expected to exhibit operating temperature variations due to the continuous ramping up and down of the generation and load. If the oil is sampled for the DGA measurement during such ramping cycles, results will not be accurate, and a fault may be reported due to a gas evolution resulting from such temporarily loading variation. This paper is aimed at correlating the generation and load ramping with the DGA measurements through extensive experimental analyses. The results reveal a strong correlation between the sampling time and the generation/load ramping. The experimental results show the effect of load variations on the gas generation and demonstrate the vulnerabilities of misinterpretation of transformer faults resulting from temporary gas evolution. To achieve accurate DGA, transformer loading profile during oil sampling for the DGA measurement should be available. Based on the initial investigation in this paper, the more accurate DGA results can be achieved after a ramping down cycle of the load. This sampling time could be defined as an optimum oil sampling time for transformer DGA.

[1]  M. Duval,et al.  Oil Additives for the Evaluation of Hot Spot Temperature in Power Transformers During Overload Tests , 1982, IEEE Transactions on Electrical Insulation.

[2]  Gary L. Orlove,et al.  Relationship between current load and temperature for quasi-steady state and transient conditions , 2000, Defense, Security, and Sensing.

[3]  Michel Duval,et al.  A review of faults detectable by gas-in-oil analysis in transformers , 2002 .

[4]  M. Duval,et al.  Calculation of DGA Limit Values and Sampling Intervals in Transformers in Service , 2008, IEEE Electrical Insulation Magazine.

[5]  E da Costa Bortoni,et al.  Extracting Load Current Influence From Infrared Thermal Inspections , 2011, IEEE Transactions on Power Delivery.

[6]  Jian Li,et al.  Gases dissolved in natural ester fluids under thermal faults in transformers , 2012, 2012 IEEE International Symposium on Electrical Insulation.

[7]  Fang Hu,et al.  The Formation Mechanism of CH4 on Nitrile Butadience Rubber in Transformer Oil , 2012 .

[8]  A. Abu-Siada,et al.  A new approach to identify power transformer criticality and asset management decision based on dissolved gas-in-oil analysis , 2012, IEEE Transactions on Dielectrics and Electrical Insulation.

[9]  A. Abu-Siada,et al.  A review on chemical diagnosis techniques for transformer paper insulation degradation , 2013, 2013 Australasian Universities Power Engineering Conference (AUPEC).

[10]  A. Abu-Siada,et al.  A new fuzzy logic approach for consistent interpretation of dissolved gas-in-oil analysis , 2013, IEEE Transactions on Dielectrics and Electrical Insulation.

[11]  A. Abu-Siada,et al.  A review of dissolved gas analysis measurement and interpretation techniques , 2014, IEEE Electrical Insulation Magazine.

[12]  Lennart Söder,et al.  Static Equivalent of Distribution Grids With High Penetration of PV Systems , 2015, IEEE Transactions on Smart Grid.

[13]  R. Sander.  Compilation of Henry's law constants (version 4.0) for water as solvent , 2015 .

[14]  Junshan Zhang,et al.  Wind Power Dispatch Margin for Flexible Energy and Reserve Scheduling With Increased Wind Generation , 2015, IEEE Transactions on Sustainable Energy.

[15]  Jun Jiang,et al.  Tracing Acetylene Dissolved in Transformer Oil by Tunable Diode Laser Absorption Spectrum , 2016, Scientific Reports.

[16]  Mahdi Khanali,et al.  A study on PD activities of oil-impregnated paper under pulse voltages using gas analysis , 2017, IEEE Transactions on Dielectrics and Electrical Insulation.

[17]  S. Eeckhoudt,et al.  Stray gassing of transformer insulating oils: impact of materials, oxygen content, additives, incubation time and temperature, and its relationship to oxidation stability , 2017, IEEE Electrical Insulation Magazine.

[18]  A. Abu-Siada,et al.  A new method to detect dissolved gases in transformer oil using NIR-IR spectroscopy , 2017, IEEE Transactions on Dielectrics and Electrical Insulation.

[19]  Dongjin Kweon,et al.  Interpretation of turn-to-turn insulation fault by dissolved gas analysis , 2018, IEEE Transactions on Dielectrics and Electrical Insulation.

[20]  P. Dyer,et al.  Dissolved gas analysis of thermal faults in transformer liquids simulated using immersed heating method , 2018, IEEE Transactions on Dielectrics and Electrical Insulation.

[21]  Osama E. Gouda,et al.  Proposed three ratios technique for the interpretation of mineral oil transformers based dissolved gas analysis , 2018 .

[22]  Leonardo P. Sampaio,et al.  Dynamic performance comparison involving grid-connected PV systems operating with active power-line conditioning and subjected to sudden solar irradiation changes , 2019 .

[23]  Chengrong Li,et al.  Multi-gas detection in power transformer oil based on tunable diode laser absorption spectrum , 2019, IEEE Transactions on Dielectrics and Electrical Insulation.

[24]  Xiaokuo Kou,et al.  A Novel Maintenance Decision Making Model of Power Transformers Based on Reliability and Economy Assessment , 2019, IEEE Access.