Sparse Measurement-Based Modelling Low-Order Dynamics for Primary Frequency Regulation

Abstract

Developing a low-order model for a target with dynamic analysis and control of the primary frequency regulation (PFR) is critical for power system stability. Measurement-based modelling is an important way to improve the model fit with practical systems. The traditional interpretation of PFR is based on the combined effect of each generator unit on the system frequency, so the response of each unit needs to be measured for modelling. With the integration of a large number, distribution and diversified frequency regulation of units into a power system, it will be a burden to measure the response of all generation units. Instead of revealing the PFR dynamics based on each generation unit, this paper illustrates the impact of all units on the frequency dynamics from the perspective of the system response. Only the system frequency is needed to construct a dataset for identifying the PFR model so that the measurement required is sparse. The effectiveness of the proposed method is verified using a test system containing multiple types of units in numerical simulations and real measurements of an actual power system.