Identification of Static and Dynamic Distribution System Aggregate Load Using Probabilistic Neural Networks

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The objectives of the proposed research are: 1) develop a neural net-based real-time load model for both dynamic and distribution system analyses; and 2) design a "smart" monitor for controlling distribution system capacitors, voltage regulators, and load tap changing transformers. The research will determine dynamic and distribution system models that represent load as a function of voltage and frequency. Tests will be made on Central Maine Power Company feeders to obtain data with which to train a probabilistic neural network. The neural network learning algorithm will be implemented with a digital signal processor in a "smart" monitor that will be able to map a vector of measured and other relevant variables to load parameters will be used for dynamic system studies and will allow real-time volt-var control on a distribution feeder. An important goal of the proposed research is to produce load models or modeling techniques that can be used in standard ways by other researchers applying well-known analysis methods.