Several smart grid applications have recently been devised in order to timely perform supervisory functions along with self‐healing and adaptive countermeasures based on system‐wide analysis, with the ultimate goal of reducing the risks associated with potentially insecure operating conditions. Real‐time transient stability assessment (TSA) belongs to this type of applications, which allows deciding and coordinating pertinent corrective control actions depending on the evolution of post‐fault rotor‐angle deviations. This study presents a novel approach for carrying out real‐time TSA based on prediction of area‐based centre‐of‐inertia (COI) referred rotor angles from phasor measurement unit (PMU) measurements. Monte Carlo‐based procedures are performed to iteratively evaluate the system transient stability response, considering the operational statistics related to loading condition changes and fault occurrence rates, in order to build a knowledge database for PMU and COI‐referred rotor‐angles as well as to screen those relevant PMU signals that allows ensuring high observability of slow and fast dynamic phenomena. The database is employed for structuring and training an intelligent COI‐referred rotor‐angle regressor based on support vector machines [support vector regressor (SVR)] to be used for real‐time TSA from selected PMUs. Besides, the SVR is optimally tuned by using the swarm variant of the mean‐variance mapping optimisation. The proposal is tested on the IEEE New England 39‐bus system. Results demonstrate the feasibility of the methodology in estimating the COI‐referred rotor angles, which enables alerting about real‐time transient stability threats per system areas, for which a transient stability index is also computed.

The growing complexity and uncertainty in modern power systems—driven by increased integration of renewable energy sources and variable loads—underscore the need for robust tools to assess dynamic stability. This paper presents an enhanced methodology for modal analysis that combines Adaptive Variational Mode Decomposition (A-VMD) with Prony’s method. A novel energy-based selection mechanism is introduced to determine the optimal number of intrinsic mode functions (IMFs), improving the decomposition’s adaptability and precision. The resulting modes are analyzed to estimate modal frequencies and damping ratios. Validation is conducted using both synthetic datasets and real synchrophasor measurements from Ecuador’s national power grid under ambient and disturbed operating conditions. The proposed approach is benchmarked against established techniques, including a matrix pencil, conventional VMD-Prony, and commercial tools such as WAProtector and DIgSILENT PowerFactory. The results demonstrate that A-VMD consistently delivers more accurate and robust performance, especially for low signal-to-noise ratios and low-energy ambient conditions. These findings highlight the method’s potential for real-time oscillation mode identification and small-signal stability monitoring in wide-area power systems.

This paper presents a novel methodology based on machine learning for assessing the power system transient stability (TS) in real-time using synchronized phasor measurements as input in order to predict the power system TS status related to the single machine equivalent (SIME) results. Based on the probabilistic models of input parameters, such as load variation and the occurrence of contingencies, Monte Carlo-type simulation is performed off-line to iteratively evaluate the system TS responses using the SIME methodology. Afterwards, the database obtained from the off-line simulations is employed for structuring and training an intelligent SIME's TS margin classifier based on support vector machines (SVM) to be used for real-time transient stability assessment (TSA). Besides, the SVM is optimally tuned by using the swarm variant of the mean-variance mapping optimization MVMO ^S . Several tests are then performed on the New England benchmark power system. Results demonstrate the feasibility and effectiveness that could be achieved in estimating the transient stability status related to the SIME's TS margins, which will also be of great value for defining suitable emergency control actions in real time.

Secure operation of power systems is receiving a renewed interest due to considerable structural changes and greater uncertainties. Thus, motivated by implementation of wide-area measurement systems in power systems worldwide, recent research work is being devoted to the development of enhanced tools for fast security assessment within reduced time frames. The work presented in this paper introduces an approach that exploits the post-disturbance data acquired via phasor measurement units (PMUs) by using a finite difference based method for the prediction of rotor angles, data-mining-based clustering for identification of critical machines, and single-machine-infinite bus (SMIB) transformation to evaluate the system transient stability in terms of unstable margin and time to instability. Numerical results obtained by applying the approach on the New England test system demonstrates the feasibility and effectiveness that could be achieved in predicting the critical machines and unstable conditions, which is also of great value for defining suitable emergency control actions.

The continuous operation and planning of electric power systems undergoes several technical and economic changes associated with environmental and societal goals toward clean, affordable, and resilient sustainable energy supply and deployment. This entails diverse upgrades, which include, for instance, integration with other energy sectors and massive addition of renewable power generation, responsive demand, and different types of storage. The dynamic properties and strength of power systems are evolving toward unprecedented levels with lowered sources for the effective management of active and reactive power balancing in different time scales. Thus, the overall security and reliability performance can be seriously compromised as unexpected disturbances may eventually cause violations to the security limits that are established for the electric power system; this may lead to the outage of important system elements and even partial or total blackouts.