A Novel Approach for Estimating Dominant Modes of Droop-Controlled Microgrids

Abstract

The increasing penetration of renewable energy sources (RESs) in modern power systems has been associated with a corresponding increase in distributed generation (DG) technologies. Hence, microgrids have become crucial building blocks in the modern electric grid as they facilitate the operation of inverter-based distributed generation (IBDG). However, the operation and control of microgrids equipped with RESs are challenging due to their associated intermittent behavior and low inertia compared to conventional synchronous generators. Generally, the modern interconnected power system is continuously subject to changes. This is due to the stochastic variations of the loads and the dynamic nature of the power system which makes the system more vulnerable to stability issues and system disturbances. These problems usually appear as poorly-damped, low-frequency oscillations (LFOs), which are regarded as one of the major problems that can negatively impact the power system’s stability.

Generally, LFOs decay quickly and the system remains stable, but the stability of the power system will deteriorate if the LFOs are poorly damped which can lead to unstable system operation and even a major system blackout. Thus, accurate identification of oscillation properties is of utmost importance for maintaining the safe operation of the power system and initiating emergency control actions that preserve the system's stability margin. Therefore, this research introduces a robust real-time stability assessment algorithm that can help network operators accurately assess networks’ stability margins and design more robust controllers that can take timely control actions, when necessary. The proposed algorithm relies on locally measured signals from the perturbed DG. Thus, it avoids dependency on data communications, which are subject to reliability issues. Further, this thesis proposes another algorithm for real-time stability margin enhancement which couples a robust stability assessment technique with a heuristic optimization algorithm in one tool. The proposed approach starts with estimating the system’s most dominant modes. If the damping ratio of these modes is below a preset threshold set by the network operator, a stability enhancement control action is executed through the adaptive tuning of the active power droop gains.

Finally, we propose a solution to the inherent problem in conventional droop controllers, where reactive power sharing among DG units is often imbalanced. This problem arises from the mismatched line impedances and the uneven distribution of loads in the network. These factors lead to variations in output voltages among DGs, the presence of circulating currents, potential overloading of smaller sources, and, in severe cases, system instability. To address this, an innovative droop control strategy is proposed that integrates the traditional droop control with a robust data-driven technique for stability assessment, enhanced by the Particle Swarm Optimization (PSO) algorithm. The proposed controller dynamically adjusts the reactive power droop gains (nq) to achieve precise reactive power sharing while simultaneously maximizing the microgrid's stability margin which is a key advantage of this approach.

Overall, the algorithms and control strategies developed in this thesis provide valuable contributions to the advancement of microgrid stability and operation, laying the foundation for more intelligent and autonomous control frameworks.

Date of Award	7 May 2025
Original language	American English
Supervisor	Tarek El Fouly (Supervisor)