|Title:||Dynamic rating of power transformers and its real time application|
Hong Kong Polytechnic University -- Dissertations
Department of Electrical Engineering
|Pages:||vii, 71 leaves : ill. ; 30 cm|
|Abstract:||Smarter asset management is a key objective of power system planners and operators. They are concerned about how to fully utilise power equipment which are operating at or approaching their 1firm capacity. One reason is high capital cost, and another is the difficulty of obtaining wayleave. On the other hand, customers can enjoy lower tariff if the reinforcement of a bulk supply point can be deferred. Therefore, there is strong interest in operating existing lines, cables and substation equipment at higher load levels. One of the major limitations on the utilisation of power transmission equipment is thermal. The maximum allowable currents or "static " thermal ratings specified by manufacturer are based on steady loading conditions under typical weather conditions. In order to calculate real time rating, one must monitor weather conditions and circuit loads, and revise the allowable loading level at regular time intervals. In addition, for equipment with long thermal time constants, one must obtain a prediction of weather and load over the next 24 hours. These variable thermal ratings are referred to in the literature as "dynamic" or "real time" thermal ratings. This study attempts to develop an effective tool to calculate the "dynamic" rating of high voltage power transformers under different cyclic load conditions and ambient temperatures so that the equipment can be operated at higher load levels without shortening its life expectancy. In addition, the calculation tools developed will be used to evaluate the supply security of any primary substation such that no "dynamic" firm capacity of transformer under study will be exceeded. An Excel program which can simulate different fault scenarios and perform real time calculations had been developed to calculate the hot-spot temperature, top oil temperature and relative aging rate of power transformers. An Artificial Neural Network had also been designed and trained to predict the daily loading of each transformer for the calculations. 1 Firm capacity of a substation equals to the total capacity installed to supply the substation minus the largest infeed. The concept is based on the N-1 security standard.|
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