Load modelling for real time monitoring of power systems

The problem of simultaneous prediciton of the load demand at all the (loading) nodes of an interconnected power system is studied. Such predictions are of obvious importance to the practising engineer for real time monitoring of the system and for economic generation scheduling. Existing prediction techniques require the use of a central computer, first for the purpose of model building and then for on-line load prediction. Both these tasks involve the processing of data being recieved from all the nodes of the system. The main purpose of the present study has been to explore the possibility of reducing the computational burden of such an exercise by developing new algorithms. A specific algorithm discussed in the text is based on the use of a two-stage filtering technique in order to replace the nonlinear estimation problem inherent in the model building task by linear estimation problems. After the model is identified, further reduction of computation is shown possible if instead of allthe data, use is made of the load data only from a few important nodes for on-line prediction of the demand at all the nodes. Some results of application of the new algorithm to real data pertaining to the Northern India grid are presented.