摘要:
The present invention comprises an apparatus and method for detecting a loss of oil from an oil-immersed transformer, based on fitting a transformer top-oil temperature model to online measurements in an iterative optimization process that yields fitted values for a first model parameter representing the top-oil temperature rise over ambient temperature and a second model parameter representing the oil time constant. Among the several advantages seen in the contemplated apparatus and method is the reduction in required instrumentation, whereby transformer oil leaks are indirectly detected without requiring pressure sensors or mechanical floats, although the presence of such sensors is not excluded by the teachings herein.
摘要:
A real time thermal monitoring and prediction system (TMPS) is provided for use in monitoring and operating a transformer. The TMPS may be used to estimate a maximum loading level for the transformer over a future time period using a dynamic thermal model for the transformer and ambient temperature forecasts. The transformer may be loaded to its maximum loading level during power congestion or a service restoration process.
摘要:
The present invention comprises an apparatus and method for detecting a loss of oil from an oil-immersed transformer, based on fitting a transformer top-oil temperature model to online measurements in an iterative optimization process that yields fitted values for a first model parameter representing the top-oil temperature rise over ambient temperature and a second model parameter representing the oil time constant. Among the several advantages seen in the contemplated apparatus and method is the reduction in required instrumentation, whereby transformer oil leaks are indirectly detected without requiring pressure sensors or mechanical floats, although the presence of such sensors is not excluded by the teachings herein.
摘要:
A real time thermal monitoring and prediction system (TMPS) is provided for use in monitoring and operating a transformer. The TMPS may be used to estimate a maximum loading level for the transformer over a future time period using a dynamic thermal model for the transformer and ambient temperature forecasts. The transformer may be loaded to its maximum loading level during power congestion or a service restoration process.