Parameter Identification Method for Composite Load Model Based on Improved Quantum Particle Swarm Optimization

Increasingly complex power systems pose new challenges to the accuracy of power grid transient stability simulation, and there is an urgent need for a new equivalent load model that can fully characterize the dynamic characteristics of power system loads, as well as corresponding parameter identification methods. A generalized comprehensive load model including photovoltaic models, distribution network feeders, and transformers is constructed. An improved quantum particle swarm optimization algorithm with targeted mutation tags is proposed, and its parameters are identified through the PSASP-Matlab joint simulation module. Comparison with genetic algorithm (GA) and baseline algorithm (QPSO) verifies the improvement of the improved algorithm in the accuracy of parameter identification of this load model.