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.

Vulnerability assessment is one of the main tasks in a Self-Healing Grid structure, since it has the function of detecting the necessity of performing global control actions in real time. Due to the short-time requirements of real time applications, the eligible vulnerability assessment methods have to consider the improvement of calculation time. Although there are several methods capable of performing quick assessment, these techniques are not fast enough to analyze real large power systems in real time. Based on the fact that vulnerability begins to develop in specific regions of the system exhibiting coherent dynamics, large interconnected power systems can be reduced through dynamic equivalence in order to reduce the calculation time. A dynamic equivalent should provide simplicity and accuracy sufficient for system dynamic simulation studies. Since the parameters of the dynamic equivalent cannot be easily derived from the mathematical models of generators and their control systems, numerical identification methods are needed. Such an identification task can be tackled as an optimization problem. This paper introduces a novel heuristic optimization algorithm, namely, the Mean-Variance Mapping Optimization (MVMO), which provides excellent performance in terms of convergence behavior and accuracy of the identified parameters. The identification procedure and the level of accuracy that can be reached are demonstrated using the Ecuadorian-Colombian interconnected system in order to obtain a dynamic equivalent representing the Colombian grid.

Operational planning of power systems, especially in terms of overall reliability and security, is a key issue in the smart grid development. Hence, it is necessary to develop new strategies to cope with increasing uncertainties arising from the fast changing ways power systems are being operated. This paper presents a comprehensive approach to determine an optimal transmission network expansion plan considering the enhancement of small-signal stability through wide-scale deployment of the existing and planned transmission system assets. The dynamic model of the transmission network operational planning (TNOP) is solved based on a combination of the Mean-Variance Mapping Optimization (MVMO), and the classic dynamic programming method embedded with a heuristic procedure. Besides, a probabilistic eigenanalysis-based recursive method is proposed to determine the optimal control strategies that are highly relevant to the enhancement of the system small-signal stability performance throughout the planning horizon. Numerical results demonstrate the viewpoint and the effectiveness of the proposed approach in providing optimal strategies of minimum cost while avoiding the instability risk associated to poorly damped low-frequency electromechanical oscillations.

In recent years, some Smart Grid applications have been designed in order to perform timely Self-Healing and adaptive reconfiguration actions based on system-wide analysis, with the objective of reducing the risk of power system blackouts. Real time dynamic vulnerability assessment (DVA) has to be done in order to decide and coordinate the appropriate corrective control actions, depending on the event evolution. This paper presents a novel approach for carrying out real time DVA, focused on Transient Stability Assessment (TSA), based on some time series data mining techniques (Multichannel Singular Spectrum Analysis MSSA, and Principal Component Analysis PCA), and a machine learning tool (Support Vector Machine Classifier SVM-C). In addition, a general overview of the state of the art of the methods to perform vulnerability assessment, with emphasis in the potential use of PMUs for post-contingency DVA, is described. The developed methodology is tested in the IEEE 39 bus New England test system, where the simulated cause of vulnerability is transient instability. The results show that time series data mining tools are useful to find hidden patterns in electric signals, and SVM-C can use those patterns for effectively classifying the system vulnerability status.