Closed Loop Load Model Identification Using Small Disturbance Data

TL;DR

This paper investigates load model identification using small disturbance data, revealing the influence of closed-loop dynamics and proposing a Kalman filter-based prediction error method for accurate parameter estimation.

Contribution

It demonstrates that load identification is inherently a closed-loop problem and develops a PEM approach with Kalman filtering for effective identification.

Findings

Closed-loop effects significantly impact load identification accuracy.

The proposed PEM with Kalman filtering accurately estimates load parameters.

Simulation results validate the theoretical analysis and method effectiveness.

Abstract

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.