This paper illustrates an earlier introduced method for systematic monitoring and control of slow electro-mechanical oscillations in electric power systems. The emphasis is on generalizing the two-area system concepts to a general multi-area power system. The method illustrates a transformed state space which explicitly models tie-line flow power dynamics between areas of interest (groups of interconnected equipment) and the rest of the system. Given parameters of an electric power grid, these tie-line power flow oscillations are expressed in terms of load and generation power deviations from given equilibrium, both controlled and uncontrolled. This new model sets the basis for identifying root causes of slow inter-area oscillations, needed to define the most important measurements and placement/enhancement of controllers for reducing these interactions. Notably, both conventional model and the transformed state space model have the same eigenvalues. The proposed approach does not require eigenmode calculations, and therefore lends itself to practical use of Phasor Measurmeent Units (PMUs). It is then shown how each area can monitor, compute and control inter-area oscillations either in an entirely decentralized way, much the same way as today's Automatic Generation Control (AGC) is carried out, or in a cooperative efficient way by jointly controlling inter-area fluctuations.

In power systems, maintaining transient stability is crucial to avoid unanticipated blackouts. The role of Transient Stability Assessment (TSA) is vital for quickly identifying and promptly addressing instabilities. TSA facilitates rapid reactions to serious fault conditions. This paper pioneers the integrated comparison of two distinct methodologies-Maximal Lyapunov Exponent (MLE) methods and Convolutional Neural Networks (CNN)-in a single unified framework for transient stability assessment in power systems, uniquely evaluating their accuracy and reliability for TSA. The CNN-based method uses measured time series data from voltage magnitude, phase angle, and frequency measurements at generator buses, while the MLE approach utilizes both phase angles and frequency of generator buses. This paper provides a qualitative and quantitative comparison of the performance and accuracy of MLE and CNN. This research utilizes the same case studies conducted on the Nordic32 system for both MLE and CNN to ensure robust, unbiased comparisons and promote interdisciplinary research, aligning with current trends in AI integration in power systems.

In power systems, ensuring transient stability is paramount to prevent unforeseen blackouts and power failures. Transient stability assessment is crucial for the early detection and mitigation of instabilities, providing a rapid response to severe fault situations. The concept of the maximum Lyapunov exponent facilitates fast predictions for transient stability assessment after severe disturbances. This paper introduces an efficient maximum Lyapunov exponent algorithm for online transient stability assessment, representing the primary contribution of this work. This approach uses the time series data from the rotor angles of generators or the phase angles of generator terminal buses. Case studies are conducted on the Nordic Power System, with simulations performed in DigSilent PowerFactory. This study contributes by offering insights into the performance and adaptability of the proposed algorithm.

This paper is motivated by the need to account for the impact of intermittent power output deviations by the renewable resources on dynamics of the changing electric energy systems. When uncontrolled, these deviations have the same effect as negative load variations. They are are hard to account for in small signal analysis using conventional state space (CSS) models. In this paper we illustrate the new transformed state space (TSS) model which represents power dynamics as a state variable. We use the Kundur two area 4 generators test system to illustrate the new model, perform its eigenmode analysis and compare with the spectral properties of the CSS model. We illustrate how the explicit modeling of power dynamics sets the basis for fundamental understanding of eigenmode structures for disconnected areas and for the interconnected system. Given these properties, we suggest that interarea dynamics can be monitored by measuring power deviations, and does not require PMUs, a major simplification. It is also illustrated how these dynamics can be monitored using just these power deviations on a commonly used two area test system.

Real-time monitoring of transient stability is crucial in power systems to avoid sudden blackout and power failure. Previously developed algorithms based on the concept of Maximum Lyapunov Exponent and Synchrophasor Measurements claim to have fast prediction of transient stability after a severe disturbance. In this paper, formerly established techniques for real-time transient stability assessment are implemented to the IEEE-39 bus test system for various fault case scenarios. Based on the obtained results from these techniques, a comparative analysis is performed to determine accuracy, effectiveness and robustness of each technique.