Particle Swarm Optimization of Dynamic Load Model Parameters in Large Systems

Particle Swarm Optimization of Dynamic Load Model Parameters in Large Systems

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This paper considers two dynamic load models that are widely used in industry to account for induction motor behavior: CMLD and CLOD.These models must be parametrized for the specific utility system in a general way so that they can be used in planning studies and provide a conservative but realistic representation of load behavior.This study considers a measurement-based color block iphone case approach to tuning both models.The load modeling study compares the response of the tuned models to generic candidate models using historical events.This study considers one area-based subsystem to simplify the modeling approach and reduce the number of models required for simulations.

Additionally, because dynamic load models often produce similar results for different sets of parameters, a sensitivity study was conducted to assess the parameter impacts on the voltage response.The sensitivity study covers the parameters that are tuned using event measurements.The process to estimate the parameters uses the particle-swarm optimization algorithm.Overall, the performance of the tuned model more accurately captures recovery voltage, ngetikin delayed recovery, and settling voltage than its predecessor models while not being overly tuned so that it remains general for peak summer conditions.