Closed Loop Load Model Identification Using Small Disturbance Data
For power system engineers, this work clarifies a fundamental issue in load modeling, showing that ignoring closed-loop effects leads to inaccurate identification when using small disturbance data.
The paper proves that load model identification using small disturbance data is inherently a closed-loop problem, where the relationship between load inputs and outputs depends on both the load itself and the equivalent network matrix. It demonstrates that the prediction error method (PEM) with Kalman filtering can effectively solve this closed-loop identification, with simulated data confirming the theoretical analysis.
Load model identification using small disturbance data is studied. It is proved that the individual load to be identified and the rest of the system forms a closed-loop system. Then, the impacts of disturbances entering the feedforward channel (internal disturbance) and feedback channel (external disturbance) on relationship between load inputs and outputs are examined analytically. It is found out that relationship between load inputs and outputs is not determined by load itself (feedforward transfer function) only, but also related with equivalent network matrix (feedback transfer function). Thus, load identification is closed loop identification essentially and the impact of closed loop identification cannot be neglected when using small disturbance data to identify load parameters. Closed loop load model identification can be solved by prediction error method (PEM). Implementation of PEM based on a Kalman filtering formulation is detailed. Identification results using simulated data demonstrates the correctness and significance of theoretical analysis.