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.

The operation of a modern electrical system presents complex operating conditions, and uncertainty factors increased by the presence of variable and flexible loads. This stochastic operating point variation can promote low-frequency oscillations, and like the conventional power system stabilizer (PSS) features static tuning, it does not provide sufficient damping. Today, an electrical system is monitored through online measurements from the wide-area measurement system (WAMS); moreover, it enables stochastic dynamics to be tracked as the remote signals of WAMS system, the time delay due to the communication channel. It is interesting to analyze the incorporation of the PSS of signals from the WAMS without the time delay effect that degrades the small-signal stability (SSS) and, in turn, allows adaptive tuning. Through two stages, at first, the subspace of the operating point is determined based on a distribution of consumption levels. Each operating point considered a random time delay. For each subspace, establish a bank of tuned PSSs. Second, through the optimal classification and the regression decision tree (CART), the classification rules for the subspace are determined, allowing classifying the measurements of WAMS. The proposed methodology is applied to the New England of 66-bus system and Ecuadorian electric system to demonstrate that the adaptive tuning of PSSs significantly improves SSS under different scenarios and is probabilistic robust, considering the time delay.

Ensuring a balance between supply and demand is critical within electricity grids, requiring a supply composition that guarantees consistent service provision in the short and medium term. Between 2008 and 2017, Ecuador’s electricity generation capacity expanded significantly, with an investment of approximately USD 8150 million into harnessing the potential energy of water. This led to the construction of five high-capacity hydroelectric projects by 2017, contributing 33.4% of the sector’s energy output by 2023. However, it is important to note that if installed hydroelectric projects operate as run-of-river plants, with limited reservoir capacity, they can only regulate water use for electricity generation on an hourly basis. As of 2023, these run-of-river plants represent 68.8% of Ecuador’s total hydroelectric capacity within the National Interconnected System (SNI). Consequently, during periods of low inflows, supplementary energy from other power generation plants is necessary to prevent energy crises. This paper addresses the impact on energy storing for electricity generation resulting from the evolution of hydroelectric power plant entry from 2006 to 2023. This aspect has not been thoroughly examined in hydrothermal systems, which primarily focus on potential energy obtained from dams. Our approach involves a statistical analysis of hydroelectric dam reservoir operational levels. We further explore the influence on demand service within Ecuador’s electricity system, particularly during observed energy crises towards the end of 2023.

Modal analysis is essential for evaluating the small-signal stability of power systems by identifying poorly damped oscillatory modes. This paper introduces an automated framework for residue computation directly within DIgSILENT PowerFactory, exploiting its internal state-space matrices and scripting environment. Unlike traditional approaches that rely on external data processing, the proposed method enables a fully integrated, repeatable, and scalable workflow for residue-guided control design. The framework automatically extracts and computes modal residues, quantifying both controllability and observability to identify the most effective control locations. Its application to benchmark systems demonstrates accurate detection of critical modes and effective damping enhancement through residue-based tuning. This integration of automated residue analysis into PowerFactory bridges theoretical modal analysis with practical implementation, offering a novel and efficient tool for oscillatory stability assessment in modern power grids.

This paper is looking to show to use of system data collected from wide-area monitoring systems (WAMS). They allow monitoring of the dynamics of power systems. Among the WAMS applications, there is the modal identification algorithm, which identifies critical oscillatory modes from PMU measurements. This application permits using data processors for estimating of frequency, damping, and amplitude of dominant mode oscillations observable in a specific electric signal (e.g., active power, frequency) recorded for the analyzed period. However, since modal identification of real-time measurements is based on an online optimization, the results usually have considerable fluctuations. Thus, it is essential to consider the complementary implementation of trend analysis for acquiring convenient early-warning indicators of oscillatory problems. This consideration allows avoiding erroneous information of the systems oscillatory behavior of the system real-time that modal identification of crude results could deliver. In this paper, the application of a l1 filter for determining the trend analysis of high-dimensional data set resulted from a commercial modal identification is explored. The algorithm is applied to an oscillatory event registered by the WAMS of the Ecuadorian National Interconnected System with promising results.