Power system load modeling parameter identification method based on grey wolf algorithm

Abstract

The invention discloses a power system load modeling parameter identification method based on a grey wolf algorithm. Aiming at the disadvantage that the identification accuracy of an existing load model parameter is insufficient, the grey wolf algorithm is adopted for optimizing to-be-identified load parameters; based on the original polynomial model, the consideration of frequency variation is added to ensure that the load characteristics can be described accurately even when the load voltage deviates from the reference voltage. Compared with a traditional static model which is too optimistic, through the input voltage and the output response of the actual system, a comprehensive load model can better reflect the load characteristics and has the characteristics of fast convergence speed and more accurate identification results. In addition, optimization performed through the grey wolf algorithm can improve the accuracy and speed of parameter identification. The grey wolf algorithm converges faster than a particle swarm optimization algorithm, and contains wobble factor, so it is not easy for the grey wolf algorithm to fall into local optimization. At the same time, the parametersneeding to be adjusted by the grey wolf algorithm are fewer. Compared with other algorithms, the grey wolf algorithm is simpler and more flexible, and can effectively improve the accuracy of parameteridentification.

Description

technical field [0001] The invention relates to the technical field of power system load model simulation, in particular to a power system load modeling parameter identification method based on the wolf algorithm. Background technique [0002] Since the 21st century, distributed power has been paid attention to and widely used, which has a great impact on the operation of distribution network. In 1996, a large-scale blackout occurred in the Northwest power system of the United States, which showed that the current model and parameters and field record results were quite different. It can be seen that the conservative static model adopted earlier in the distribution network can no longer describe the operating characteristics of the load model well. In the operation of the power system, an inaccurate load model will lead to misoperation and collapse of the system, so the accuracy of the model should be improved as much as possible. [0003] Earlier, the power system adopted...

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Problems solved by technology

[0004] Aiming at the problem of insufficient identification accuracy of current power system load model parameters, the present invention proposes a power system load modeling parameter identification method based on Canglang algorithm. The method is simple and flexible, and can improve the speed and speed of power system load model parameter identification. accuracy

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