Algorithm of Probability Distribution Law for Steady State Mode and Fault Condition Parameters of Electric Power System
DOI:
https://doi.org/10.14529/power180402Keywords:
electric power system, numerical methods, probability distribution law, random variable, quantile, functional de-pendencyAbstract
While considering certain problems in the electric power industry, the considered parameters, i.e. Steady State Mode and Fault Condition Parameters, operating values, etc. are the functional dependents of many arguments. Establishing the full probabilistic characteristics shall work to build up the understanding of all possible states, in which an object can exist. The challenge of their obtaining lies in the complexity of accounting for all the relationships between the random initial data when solving classical statistical methods, which does not have a full solution.
The article presents the algorithms developed to apply the selection of interval boundaries of input and output data method to determine the probability distribution law for steady state mode and fault condition parameters of electric power system. A peculiar feature of these algorithms is the use of a universal total of random initial data consisting of quantiles of discrete orders. Thus, the number of universe general total variants can be substantially reduced, but the accuracy of the probabilistic characteristics formation is unchanged.
The tasks were solved using the probability theory statement and mathematical statistics particularly
in power engineering, numerical optimization methods. Also, MATLAB software package was used to solve
the technical calculation problems.
Determining the probabilistic characteristics of electrical dependencies in nondeterminate form allows executing the calculations practice in the electric power industry and electrical engineering, when the results are full and guaranteed, which allows for objective and reliable optimization and the precision of risks and accident event assessment. Software algorithms implementation solve a number of practical tasks, such as determining the probability laws for the risks of overload, emergency loss, and destruction.
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