An intelligent system for short-time loading capability assessment of transmission lines

This paper describes an application of the intelligent system (IS) combining an expert system (ES) and an artificial neural network (ANN) for the evaluation of the short time thermal rating and temperature rise of overhead power transmission lines. The IS was developed as a rule-based system using the Leonardo expert system shell in conjunction with a neural network and database. The ANN and regression best-fitting techniques were employed to determine the hourly solar irradiance. The neural network was trained for the prediction of maximum hourly values of the direct and diffuse solar radiation dependent on astronomic and meteor-climatic conditions. The developed IS can be used to assist operators in loading of transmission lines in different operating, ambient, geographic latitude, cloud and ground reflection conditions. It also assists the operators to determine the permissible duration of the conductor overload.