Comparison of dynamic load modeling using neural network and traditional method

The representation of load dynamic characteristics remains an area of great uncertainty and it becomes a limiting factor of power systems dynamic performance analysis. A major difficulty, both for component-based and measurement-based methods, is the lack of data for dynamic load modeling. A way of solving this problem for measurement-based methods is to interpolate and extrapolate the models identified from wide voltage variation data recorded during naturally-occurring disturbances or field experiments. This paper deals with data measured in Chinese power systems using two models: a multilayer feedforward neural network (ANN) with backpropagation learning, and difference equations (DE) with recursive extended least square identification. A comparison between the two approaches was done. The results show that the DE models interpolation and extrapolation are nearly linear, and they cannot describe the voltage-power nonlinear relationship of load dynamic characteristics. However, the ANN models can represent well this nonlinear relationship, they are promising dynamic load models

Published in:
ANNPS '93. Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems (Cat. No.93TH0532-2), 253 - 8
China;interpolation;extrapolation;load forecasting;dynamic load modeling;dynamic characteristics;power systems;performance analysis;disturbances;field experiments;multilayer feedforward neural network;backpropagation learning;difference equations;recursive extended least square identification;

 Record created 2007-04-04, last modified 2018-03-18

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