Online inertia estimation for power systems with high penetration of RES using recursive parameters estimation

Abstract

Low and time-changing inertia values due to the high percentage of renewable energy sources (RESs) can cause stability problems in power systems due to rapid frequency instabilities. Inertia monitoring will assist operators to apply suitable actions and more proper control methods to alleviate stability issues. Therefore, this paper proposes an online method to estimate the total inertia of a network using a recursive least-squares approach. The proposed method uses network measurements with a non-recursive system identification approach to initially estimate the network hypothesis model. Then, the recursive method is used together with time changing measurements to recursively estimate model parameters and extract online inertia estimates of the network. During the estimation, the method does not need to store previous data after each sample step; therefore, the computation burden is significantly reduced. More importantly, the technique incorporates the use of available electromechanical oscillation modes in the system, which are linked with system parameters, to determine the network inertia estimates. The applicability of the proposed method has been validated by numerical simulations of the IEEE 39-bus network and the aggregated New Zealand network with its actual inertia data.

Assessment of the Center of Inertia and Regional Inertia with Load Contribution via a Fully Data-Driven Method

“…However, the accuracy may be influenced by topology changes that shift the modes' frequencies. In [20], the equivalent inertia of a power system is estimated using the recursive least-squares (RLS) by fitting oscillation data, where an initial model is estimated with a non-recursive system identification method. The authors in [21] estimate the inertia of areas from the 60Hz Japan system using frequency and rate of change of frequency measurements applying a frequency spectrum and performing mode shape analysis.…”

Power system inertia estimation method based on maximum frequency deviation

“…Small disturbance method is based on system identification method and sample date of small disturbance power and frequency [7,8]. It usually relies on phasor measurement unit (PMU) for frequency measurement and the supervisory control and data acquisition (SCADA) for power measurement [9], and has high requirements for data volume and data quality Frequency measurements FIGURE 1 Flow chart of inertia estimation method based on RoCoF [10].…”