Failure of a transformer is a costly incident which can impact power system operation and result in loss of supply to customers.Transformers can fail through a number of mechanisms,one of which is from the formation o...Failure of a transformer is a costly incident which can impact power system operation and result in loss of supply to customers.Transformers can fail through a number of mechanisms,one of which is from the formation of bubbles within the insulation.Currently,the loading guidance is set to avoid certain overload temperatures as high temperature is the main cause of bubbles forming.This paper analyses the limit set within the loading guides,comparing the most commonly cited temperature limit of 140℃ with values taken from across the full spectra of available studies in literature.It is concluded that there is sufficient evidence for further investigation into formation of bubbles from transformer insulation as employing a single temperature limit may be inadequate.Use of a single temperature value is unsuitable,given the wide range of potential scenarios that the transformer fleet can present.It has become the go-to rationale that the moisture content of the solid insulation is the key driver for transformer bubbling temperature.This is true,however,it is shown that other factors such as solid insulation aged condition(determined through degree of polymerisation value)and the insulation type are also important considerations.展开更多
Frequency response analysis (FRA) is regarded as the most effective technique to detect mechanical faults of transformers. Over the years, FRA measurement data have been collected by utilities into transformer asset d...Frequency response analysis (FRA) is regarded as the most effective technique to detect mechanical faults of transformers. Over the years, FRA measurement data have been collected by utilities into transformer asset databases. The characteristic of FRA data is fundamentally determined by the transformer's equivalent electrical circuit, which consists of inductance and capacitance parameters that are windings' design and structure dependent. Different winding types tend to have different FRA characteristics, and a transformer's design information such as winding type, dimension etc. is often not known to the utility but critically important for asset management. This study reviews the state-of-the-art transformer FRA databases and application of machine learning techniques in this field, and proposes to apply a support vector machine (SVM) model onto the FRA data to identify the winding type. The SVM model is first trained by FRA traces of transformers with known winding types, and after testing, the SVM model is then applied to FRA traces with unknown winding information. A set of data from the UK's National Grid FRA database, was used to demonstrate and verify the SVM model. All transformers used in this study are 400/275/13 kV transmission transformers, which were designed using four different winding types, namely multiple layer, plain disc, interleaved disc and single helical windings. The proposed method can successfully identify the correct winding type.展开更多
基金J.Hill also acknowledges financial support from EPSRC(EP/L016141/1)through the Power Networks Centre for Doctoral Training.
文摘Failure of a transformer is a costly incident which can impact power system operation and result in loss of supply to customers.Transformers can fail through a number of mechanisms,one of which is from the formation of bubbles within the insulation.Currently,the loading guidance is set to avoid certain overload temperatures as high temperature is the main cause of bubbles forming.This paper analyses the limit set within the loading guides,comparing the most commonly cited temperature limit of 140℃ with values taken from across the full spectra of available studies in literature.It is concluded that there is sufficient evidence for further investigation into formation of bubbles from transformer insulation as employing a single temperature limit may be inadequate.Use of a single temperature value is unsuitable,given the wide range of potential scenarios that the transformer fleet can present.It has become the go-to rationale that the moisture content of the solid insulation is the key driver for transformer bubbling temperature.This is true,however,it is shown that other factors such as solid insulation aged condition(determined through degree of polymerisation value)and the insulation type are also important considerations.
文摘Frequency response analysis (FRA) is regarded as the most effective technique to detect mechanical faults of transformers. Over the years, FRA measurement data have been collected by utilities into transformer asset databases. The characteristic of FRA data is fundamentally determined by the transformer's equivalent electrical circuit, which consists of inductance and capacitance parameters that are windings' design and structure dependent. Different winding types tend to have different FRA characteristics, and a transformer's design information such as winding type, dimension etc. is often not known to the utility but critically important for asset management. This study reviews the state-of-the-art transformer FRA databases and application of machine learning techniques in this field, and proposes to apply a support vector machine (SVM) model onto the FRA data to identify the winding type. The SVM model is first trained by FRA traces of transformers with known winding types, and after testing, the SVM model is then applied to FRA traces with unknown winding information. A set of data from the UK's National Grid FRA database, was used to demonstrate and verify the SVM model. All transformers used in this study are 400/275/13 kV transmission transformers, which were designed using four different winding types, namely multiple layer, plain disc, interleaved disc and single helical windings. The proposed method can successfully identify the correct winding type.
