Artificial Neural Network-based Distribution Substation and Feeder Load Forecast
نویسندگان
چکیده
Artificial Neural Networks (ANNs) have been successfully applied to the problem of forecasting future load values, especially in the short term framework (a few minutes to a few hours ahead). Traditional analytical models have shown difficulties when dealing with (i) the highly variable demand curve shapes, (ii) some independent variables that exhibit random behaviour, and (iii) the identification of variables that could explain relevant load variations, such as weather variables. Current available ANN applications to this problem are by far aimed at a systemwide level, where the load behaviour is more regular than at substation or even primary feeder levels. This work presents the application of an ANN-based methodology for forecasting load values in two time frames, namely one or more 15-minute intervals and 24 hours. Input variables are current and past values of demand and ambient temperature. Output variables are forecasted (future) values of demand. Demand data can be originated either from distribution substation transformers or from primary feeders. This methodology has been implemented as a software tool which is currently running on a local computer in the Campinas Centro substation. This is one of the most important CPFL’s distribution substation, and is equipped with three 138/11.9-kV, 40-MVA transformers. Input values are made available through the substation’s data-acquisition system. Results obtained with this implementation are very encouraging, even when using as little historical data as 3 months. Forecast error is also very low when a demand curve substantially different from the ones presented to the Artificial Neural Network in its training phase are used in the processing mode. A separate module for dealing with load transfers between primary feeders during contingencies is currently in its final stages of development. ARTIFICIAL NEURAL NETWORK-BASED DISTRIBUTION SUBSTATION AND FEEDER LOAD FORECAST J. Yasuoka J. L. P. Brittes H. P. Schmidt J. A. Jardini 1 Escola Politécnica da Universidade de São Paulo Brazil 2 Companhia Paulista de Força e Luz CPFL Campinas, SP Brazil
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