Due to the rapid growth of renewable energy and its integration into the existing grid, the future power grid may face several stability challenges. Sub-synchronous oscillation (SSO) is one of the critical stability aspects that can disrupt the normal operation of the grid within a few grid cycles. Although the methods of SSO detection and its mitigation in conventional power systems are well defined, the interfacing of large number of power electronics-based devices at generation, transmission and distribution level has regenerated SSO issue introducing several new factors that can trigger different types of SSO. Thus, this paper proposes an energy based SSO assessment that analyzes the energy of the measured signal before and after the disturbance to effectively determine SSO in a timely manner. This approach is validated on both simpler and more complex power systems including single AC bus integrated with type-4 wind farm and standard IEEE 39 Bus network with type 4 wind farm, respectively. Various simulation case studies are presented to validate the effectiveness of proposed scheme in determining different types SSOs.

Transient Stability (TS) remains a critical concern in ensuring secure operation of modern power systems, particularly with the growing complexity introduced by renewable energy integration, power electronic devices, and hybrid AC/DC infrastructures. Traditional methods for transient stability assessment (TSA) and transient stability-constrained optimal power flow (TSC-OPF), such as time-domain simulations and energy function-based techniques, face limitations in scalability, computational efficiency, and real-time applicability. This paper presents a holistic review of Machine Learning (ML) approaches adopted for TSA and TSC-OPF, covering a range of models from traditional learners (e.g., support vector machines, decision trees) to DL (e.g., convolutional neural networks, long short-term memory networks, graph neural networks) and Reinforcement Learning (RL) techniques. For each domain, methodologies will be categorized, highlighting key advancements, and discussing trade-offs in performance. Furthermore, existing challenges are identified and future research directions are proposed, emphasizing on hybrid modeling, uncertainty handling, real-time assessment and RL challenges. This review aims to serve as a timely reference for researchers and practitioners working on data-driven solutions for power system TS.

The modern power system dominated by renewable energy resources such as Type-4 wind farms (WF) has recently seen significant increase in cases of sustained oscillations at the sub-synchronous frequency range that is typically referred to sub-synchronous oscillations (SSOs) in the existing literature. Although thorough studies have been conducted on prior types of WFs to understand the triggering factors for SSO, however, in the case of Type-4 WFs the triggering factors and causes still remain unclear. Therefore, this paper studies the SSO characteristics exhibited by Type-4 WFs operating within a weak grid. To emulate real-world grid conditions, an extensive AC network modeled after the IEEE 39-bus system is employed. Through simulations incorporating the integration of Type-4 WFs at various locations within the grid, the resulting effects on SSO triggering factors are analyzed. The investigation is structured around three distinct case studies, conducted at buses 31, 37, and 38. These studies involve varying network reactance to explore and establish the relationship between SSO events and the short-circuit ratio (SCR) of the network. The consistent findings across diverse case studies underscore the general relationship between SCR, grid strength, and SSOs. The analysis reaffirms the significant impact of increased reactance on SSO characteristics and confirms that weaker grids are more susceptible to SSOs.

Packed-U-Cell (PUC) is a single DC source multi-level inverter that can be used in many applications such as grid-connected photovoltaic (PV) systems. In this application, the total harmonic distortion (THD) of the generated current signal should be minimized while operating at the unity power factor (maximum active power transfer). These objectives can be achieved by regulating the auxiliary capacitor voltage around its reference value while tracking the reference current signal that varies with the PV maximum power point (MPP). Thus, this paper proposes a reinforcement learning (RL) based controller that satisfies the aforementioned control objectives using the actor-critic RL architecture and the proximal policy optimization (PPO) learning algorithm. The designed RL-based controller is applied on a single-phase 5-level PUC inverter. The proposed design is validated through simulations where the obtained control policy resulted in a maximum absolute voltage error of 1.9 V and a THD value of 2% and 4.5% for reference current peak values of 8.8A and 4.2A, respectively. Furthermore, the proposed RL-based controller shows high robustness to parameter variations (different capacitor and inductor sizes).