Generalized Participation Factors on Nonlinear Power System Oscillations

This work investigates the linear and nonlinear participation factors in power system oscillations, introducing novel model-based and measurement-based approaches for stability analysis. From the measurement perspective, this research proposes a method for estimating participation factors from generator response measurements under diverse disturbances. The devised technique computes extended participation factors that align precisely with model-based factors, given that the measured responses satisfy an ideally symmetric condition. The symmetric condition is further relaxed by identifying a coordinate transformation from the original measurement space to an optimally symmetric space, thereby achieving the ideal estimation of participation factors from measurements alone. The effectiveness of the proposed approach is demonstrated comprehensively on a two-area system before being tested on a 48-machine power system from the Northeast Power Coordinating Council (NPCC). Given that measurement-based PFs often necessitate considerable data and a black-box system model, the study also proposes response-based PFs for system application, including Electromagnetic Transients (EMT) simulations. Additionally, this research introduces an Extended Prony Analysis method for measurement-based modal analysis. Drawing upon normal form theory, it juxtaposes analyses on transient and post-transient waveforms, distinguishing resonance modes triggered by near-resonance conditions from natural modes. This method provides more precise modal properties than traditional Prony Analysis, particularly in the case of near-resonance disturbances. From the model-based perspective, this research scrutinizes the limitations of existing nonlinear PFs, advocating for Time-Variant Nonlinear Participation Factor (TNPF). The relationships between PFs and NPFs are examined in detail from three aspects: perturbation amplitude, time dimension, and nonlinear mode. Additionally, the uniqueness of linear and nonlinear PFs is proven by introducing scaling factors. To bridge the discontinuity between linear and nonlinear PFs, two steps are taken: introducing a time decaying factor to address perturbation amplitude and time dimension, and defining a nonlinear mode via convolution, considering the influence from resonances. The resulting TNPF is presented, with its efficacy demonstrated through a case study

Online coherency identification and stability condition for large interconnected power systems using an unsupervised data mining technique

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 K -means 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

Analysis of Dynamic Mode Decomposition

In this master thesis, a study was conducted on a method known as Dynamic mode decomposition(DMD), an equation-free technique which does not require to know the underlying governing equations of the complex data. As a result of massive datasets from various resources, like experiments, simulation, historical records, etc. has led to an increasing demand for an efficient method for data mining and analysis techniques. The main goals of data mining are the description and prediction. Description involves finding patterns in the data and prediction involves predicting the system dynamics. An important aspect when analyzing an algorithm is testing. In this work, DMD-a data based technique is used to test different cases to find the underlying patterns, predict the system dynamics and for reconstruction of original data. Using real data for analyzing a new algorithm may not be appropriate due to lack of knowledge of the algorithm performance in various cases. So, testing is done on synthetic data for all the cases discussed in this work, as it is useful for visualization and to find the robustness of the new algorithm. Finally, this work makes an attempts to understand the DMD\u27s performance and limitations better for the future applications with real data