symbolic dynamic models for highly varying power system loads

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the voltage response of the load as they provide necessary perturbation for voltage and
frequency. The cross correlation method, used in the proposed model, is based on the fact
that for a linear system with certain input signals the cross correlation of the system input
and output will give the impulse response. The simulation results show that the approach
is practical and accurate for power system load modeling.
A new modeling technique, based on real data obtained on site in industrial power
systems was introduced in 1998 by Wu and Wen. [3] This technique is a combination of
genetic algorithm, evolutionary programming and evolution strategies. Different from
conventional optimization techniques, genetic algorithm is a population-based algorithm.
The population of all possible solution sets is generated in a stochastic manner and the
best-found solution set acts as parents for successive generations. This technique, called
intelligent learning technique is based on system measurement. The authors proposed an
equivalent area load model and to optimize the parameters of this model, an improved
genetic algorithm (GA) was developed. Simulation results show that this method is
capable of finding a precise model for the load area in a real power system. Moreover it is
a practical method as the data required for this can be easily measured in a power system.
Ju, Handschin and Karlsson published a paper on nonlinear dynamic load
modeling using GA in 1995 [4]. Many nonlinear dynamic models (NLDM) have been
developed in the past. Composite induction motor (IM) models have long been studied
and are popular. The effect of voltage phase angle on load power is considered in the IM
model but it has not been taken into consideration in the input output (IO) models. The
IO models can represent induction motor loads as well as the other dynamic loads. This