Abstract
Simulation of electric load is one of the most difficult modeling problems of operating condition of the electric power system. It is stipulated by a large number and a variety of electrical loads and their erratic behavior. The considerable share of loads cannot be described with sufficient accuracy by typical characteristics. To refine and actualize them, special staged field tests are carried out in power systems. This does not completely solve the problem. It is impossible to cover all loads in all possible states with such experiments. The purpose of the work is to develop a method for determining the static load model of electric power systems by voltage. The determination is made by mathematical processing of the data of steady-state observation over the parameters of an electrical mode. The method is based on the developed mathematical model considering probability nature of changes in the load power and the supply network voltage as well as the correlations between them. There are two such correlations. Changing the load power caused by a voltage variation. And also a voltage variation at the supply point caused by the change in the load power. The first correlation reflects the natural static load model. The second correlation reflects the "network response". Taking into account the "network reaction" is necessary when processing data from the steady-state measurement. The difference of the proposed method is usage of laws of probability distribution instead of the measured values. This makes it possible to use the method in combination with cluster analysis methods. In these methods, the measurement results are presented in the form of a Gaussian mixture. The proposed method can be used to automate the process of determining the static load models according to the measurements without conducting staged field tests. This will make it possible to cover all the telemetered nodes of load in all possible states in the future.
Keywords
Electric power system, static load model, steady-state measurements, cluster analysis, network response, system of random variables, covariance matrix, dispersion ellipse.
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