REACTIVE POWER FORECASTING

摘要

The present work has as main purpose the development of a new short-term reactive power hourly forecast technique, which can be used at utility or substations levels. The proposed model, named A Hybrid Model for Reactive Forecasting, is divided in two stages. In the first stage, the active and reactive power data are classified by an unsupervised neural network - the well known Self-Organized Maps of Kohonen (SOM); In the second stage, a Lag Distributed Autoregressive Model (ADL) is used with its parameters estimated by an Iteratively Reweighted Least Square (IRLS). It also includes a correction lag structure for serial autocorrelation of the residuals as used in the Cochrane-Orcutt formulation. The proposed model is applied to real data of one substation and the results are compared with two other approaches.