Publications

Abstract not provided.

In low inertia grids, significant frequency deviations can occur as a result of changes in power (load, generation, etc.), These deviations may activate various protection schemes designed to safeguard the system, potentially leading to blackouts. Therefore, assessing the frequency stability of the power system is crucial. The Frequency Security Index (FSI) serves as a metric for evaluating system stability. However, computing the FSI for a specific load change necessitates actual load changes on the system, which is often impractical. This paper introduces a method for calculating the FSI without requiring load changes for all values. A mathematical expression for the FSI is derived, which uses the values of microgrid parameters (such as inertia and damping constant) to compute the FSI for any load change. Subsequently, the parameters that most significantly affect the FSI are identified. Then, the paper introduces a Moving Horizon Estimation (MHE)-based parameter estimation approach, which leverages small perturbations from an energy storage system to estimate the most influential parameters for the FSI. The results show that the FSI calculation with the estimated parameters is more accurate (compared to COI averaged parameters), enabling a more effective state of health monitoring of the microgrid.

In low inertia grids, significant frequency deviations can occur as a result of changes in power (load, generation, etc.), These deviations may activate various protection schemes designed to safeguard the system, potentially leading to blackouts. Therefore, assessing the frequency stability of the power system is crucial. The Frequency Security Index (FSI) serves as a metric for evaluating system stability. However, computing the FSI for a specific load change necessitates actual load changes on the system, which is often impractical. This paper introduces a method for calculating the FSI without requiring load changes for all values. A mathematical expression for the FSI is derived, which uses the values of microgrid parameters (such as inertia and damping constant) to compute the FSI for any load change. Subsequently, the parameters that most significantly affect the FSI are identified. Then, the paper introduces a Moving Horizon Estimation (MHE)-based parameter estimation approach, which leverages small perturbations from an energy storage system to estimate the most influential parameters for the FSI. The results show that the FSI calculation with the estimated parameters is more accurate (compared to COI averaged parameters), enabling a more effective state of health monitoring of the microgrid.

Frequency stability issues are common in low-inertia microgrids due to the dominance of power electronics converter-based resources and comparatively low levels of inertia of synchronous generation. While many methods have been proposed for frequency support in these systems, it is challenging to ensure both stability and an adequate level of support without directly modeling the system. Perturbation-based extremum-seeking control (PESC) is a model-free adaptive control strategy that optimally sets the system's performance measure without requiring a mathematical system model. However, PESC offers poor transient performance due to design limitations, such as an averaging theory. Some modifications have been made to address these limitations; nevertheless, the modified design of PESC operates more as a model-based control. The Quasi-Newton method is a popular class of numerical optimizers attributed with a design procedure similar to model-free control that uses the gradient and an approximate inverse Hessian of the performance measure to run an optimization loop. This paper presents the design and compares the performance of the Quasi-Newton method and a model-based PESC for frequency support of microgrids. The simulation results illustrate the comparable performance of both control schemes and show the model-free control capability of the numerical optimization method for a class of nonlinear dynamic systems.