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Identification of coherent generators and the determination of the stability system condition in large interconnected power system is one of the key steps to carry out different control system strategies to avoid a partial or complete blackout of a power system. However, the oscillatory trends, the larger amount data available and the non-linear dynamic behaviour of the frequency measurements often mislead the appropriate knowledge of the actual coherent groups, making wide-area coherency monitoring a challenging task. This paper presents a novel online unsupervised data mining technique to identify coherent groups, to detect the power system disturbance event and determine status stability condition of the system. The innovative part of the proposed approach resides on combining traditional plain algorithms such as singular value decomposition (SVD) and Kmeans for clustering together with new concept based on clustering slopes. The proposed combination provides an added value to other applications relying on similar algorithms available in the literature. To validate the effectiveness of the proposed method, two case studies are presented, where data is extracted from the large and comprehensive initial dynamic model of ENTSO-E and the results compared to other alternative methods available in the literature. [ABSTRACT FROM AUTHOR]

• The padé method is extended to identifying the harmonic analysis of multiple signals. • Harmonic pollution propagation in modern electrical grids is determined by exploiting the spatio-temporal characteristics of the padé approach. • A visualization tool that provides to system operators the path of harmonic propagation. This paper discusses the application of a spatio-temporal analysis method based on the Padé framework to analyze harmonic distortion in electrical networks. Using the observation matrix of three-phase voltage profiles, a polynomial in the z -domain is obtained for each profile. and their Padé approximation produces a discrete transfer function, which captures the dominant harmonics embedded in the signals. The application for a wide area of harmonic monitoring is proposed by extending the Padé approximant to the multi-signal case. By assuming that all signals have a common set of frequencies, the Padé approximation is efficiently computed for all the measurements. The best set of poles is extracted by the minimum singular value decomposition of an extended Hankel matrix. Due to its multiscale nature, the method is well suited for the analysis and monitoring of harmonic distortion in electrical networks. The developed methodology is compared with various analytical approximation methods using simulated noisy signals, proving that it is robust against the most common type of harmonic perturbations. Moreover, the proposed technique is applied to simulated data from a three-phase IEEE 14-bus test system, where the harmonic penetration and propagation is also discussed. Finally, the Padé method is validated on real measured signals. [ABSTRACT FROM AUTHOR]