Refined identification of the key parameters of power system synthesis load model based on the improved butterfly algorithm

With the improvement in power grid simulation accuracy requirements, the existing typical load model parameters can no longer meet the accuracy requirements and become the short board that restricts the stable operation of power system. This paper mainly proposes an improved butterfly optimization algorithm based on the population optimization and dynamic strategy (PODSBOA) for commonly used synthesis load model (SLM) parameters to realize the refined and personalized identification of SLM key parameters:[ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:msub><mml:mi>p</mml:mi><mml:mi>u</mml:mi></mml:msub></mml:mrow></mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"><mml:mrow><mml:msub><mml:mi>q</mml:mi><mml:mi>u</mml:mi></mml:msub></mml:mrow></mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m3"><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mi>s</mml:mi></mml:msub></mml:mrow></mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m4"><mml:mrow><mml:msub><mml:mi>X</mml:mi><mml:mi>s</mml:mi></mml:msub></mml:mrow></mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m5"><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mi>r</mml:mi></mml:msub></mml:mrow></mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m6"><mml:mrow><mml:msub><mml:mi>X</mml:mi><mml:mi>r</mml:mi></mml:msub></mml:mrow></mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m7"><mml:mrow><mml:msub><mml:mi>K</mml:mi><mml:mi>m</mml:mi></mml:msub></mml:mrow></mml:math> , and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m8"><mml:mrow><mml:msub><mml:mi>M</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>f</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> ]. The results indicate that in the 2-s load data experiment, the identification error is 0.02, the identification accuracy is 4.09, and the convergence time of the PODSBOA is 12.048 s. In the 5-s load data experiment, the identification error is 0.013, the identification accuracy is 6.65, and the convergence time of the PODSBOA is 23.405 s. The identification errors in the two sets of experiments are reduced by 0.02023–0.06443 compared with other algorithms. The comparison results of different load model parameter identification algorithms indicate that the improved PODSBOA proposed in this paper has high recognition accuracy and fast convergence speed and solves the problem of low accuracy and instability of the identification results of the existing identification schemes.