TY - JOUR
T1 - Propagating uncertainty using IEEE STD C57.104-2019 dissolved gas analysis methodology for transformers
AU - Hosseini, Michael
AU - Stewart, Brian G.
PY - 2024/8/6
Y1 - 2024/8/6
N2 - Dissolved Gas Analysis is a well-established tool for transformer health monitoring with published Standards to help with its interpretation. However, even though it is known that there is measurement uncertainty regarding the true value of sampled gas, there is less available guidance regarding the practical implications. This paper proposes a method for propagating the measurement uncertainties through the methodology presented in the IEEE Std C57.104-2019 to provide the degree of support for its potential outputs. This is done relying on the simplifying assumption that the measurement uncertainty can be expressed as a symmetric triangular distribution for a given gas sample, and that gas samples are independent. The joint probability function is derived in general terms analytically and then a stratified sampling approach is proposed to numerically solve the function. In addition, a modification is made to allow for a more constrained sampling space by deriving and using a simplifying marginal probability without impacting accuracy. These are presented via a use of a case study to demonstrate the efficacy of the proposed approaches.
AB - Dissolved Gas Analysis is a well-established tool for transformer health monitoring with published Standards to help with its interpretation. However, even though it is known that there is measurement uncertainty regarding the true value of sampled gas, there is less available guidance regarding the practical implications. This paper proposes a method for propagating the measurement uncertainties through the methodology presented in the IEEE Std C57.104-2019 to provide the degree of support for its potential outputs. This is done relying on the simplifying assumption that the measurement uncertainty can be expressed as a symmetric triangular distribution for a given gas sample, and that gas samples are independent. The joint probability function is derived in general terms analytically and then a stratified sampling approach is proposed to numerically solve the function. In addition, a modification is made to allow for a more constrained sampling space by deriving and using a simplifying marginal probability without impacting accuracy. These are presented via a use of a case study to demonstrate the efficacy of the proposed approaches.
KW - constrained sampling space
KW - propagating uncertainty
KW - measurement uncertainty
KW - stratified sampling approach
KW - symmetric triangular distribution
KW - transformer health monitoring
U2 - 10.1049/icp.2024.0549
DO - 10.1049/icp.2024.0549
M3 - Conference article
SN - 2732-4494
VL - 2023
SP - 396
EP - 401
JO - IET Conference Proceedings
JF - IET Conference Proceedings
IS - 46
T2 - 23rd International Symposium on High Voltage Engineering (ISH 2023)
Y2 - 28 August 2023 through 1 September 2023
ER -