Dynamic loading of power components in a deregulated electricity market requires reliable models that are able to predict the thermal behaviour when the load exceeds a particular value. The thermal stress of the components is known to be the most critical factor to the assessment of network load capability. Predicting the evolution of the thermal stress during overload conditions is essential to estimate the loss of insulation life and to evaluate the consequent risks of both technical and economical nature. The paper discusses an innovative grey-box architecture for integrating physical knowledge modelling (also know as white-box) with machine learning techniques (also known as black-box) in dynamic load capability assessment of power components. To evaluate the effectiveness of the proposed solution, a specific case study concerning a system of medium-voltage power cables is presented.

Dynamic loading of power components in a deregulated electricity market requires reliable models that are able to predict the thermal behaviour when the load exceeds a particular value. The thermal stress of the components is known to be the most critical factor to the assessment of network load capability. Predicting the evolution of the thermal stress during overload conditions is essential to estimate the loss of insulation life and to evaluate the consequent risks of both technical and economical nature. The paper discusses an innovative grey-box architecture for integrating physical knowledge modelling (also know as white-box) with machine learning techniques (also known as black-box) in dynamic load capability assessment of power components. To evaluate the effectiveness of the proposed solution, a specific case study concerning a system of medium-voltage power cables is presented.

Semiphysical modelling architecture for dynamic assessment of power components loading capability

VACCARO A;D. VILLACCI
2004

Abstract

Dynamic loading of power components in a deregulated electricity market requires reliable models that are able to predict the thermal behaviour when the load exceeds a particular value. The thermal stress of the components is known to be the most critical factor to the assessment of network load capability. Predicting the evolution of the thermal stress during overload conditions is essential to estimate the loss of insulation life and to evaluate the consequent risks of both technical and economical nature. The paper discusses an innovative grey-box architecture for integrating physical knowledge modelling (also know as white-box) with machine learning techniques (also known as black-box) in dynamic load capability assessment of power components. To evaluate the effectiveness of the proposed solution, a specific case study concerning a system of medium-voltage power cables is presented.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12070/1971
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