An eigensystem realization algorithm based data-driven approach for extracting electromechanical oscillation dynamic patterns from synchrophasor measurements in bulk power grids

This study proposes a novel spatial‐temporal decomposition approach for systematically tracking dominant modes, mode shapes and coherent groups in bulk power systems using measurement data. First, components of the dominant oscillation modes, including frequencies and damping ratios, are identified from measurement data through a proposed recursive continuous wavelet transform. Second, cross‐wavelet transform is employed to estimate the mode shapes using the wavelet coefficient of the dominant modes. Furthermore, a reconstructed wavelet coefficient, which integrates the wavelet coefficients of all the estimated dominant modes, is used to identify the coherent groups of generators via the cross‐correlation coefficient. The proposed approach is evaluated on the simulation data from 16‐machine 68‐bus test system and China Southern Power Grid (CSG) as well as the field‐measurement data collected from phasor measurement units of CSG. It is demonstrated that the proposed approach performs with high accuracy, robustness and efficiency in tracking dominant modes, mode shapes and coherent groups in the bulk power systems.

Multi‐order stochastic subspace identification (MOSSI) has been extensively used to estimate the electromechanical oscillation modes from probing, ambient and ringdown data. It has been validated with good performances, while the computational burden is still a major obstacle. This study develops a fast iterative MOSSI (FSSI) approach for computational enhancement of MOSSI in mode estimation. In the proposed approach, an initial cluster of eigenvalues is formulated through FSSI with repetitive calculations (RCs), and electromechanical oscillation mode separation (EOMS) is utilised to discriminate the electromechanical modes. The RCs within the FSSI is calculated through changing the model order successively over the defined range given by a mean of singular values based order determination strategy. Additionally, the proposed approach is highly reliable against prevalent measurement noises owing to RCs and the EOMS. The performance of the proposed method is evaluated in Kundur's two‐area test system by comparing with MOSSI, Prony and autoregressive moving average exogenous. Its applicability for both the ringdown and ambient data is also demonstrated with the phasor measurement units field‐measurement data from the China Southern Power Grid. The results confirmed the accuracy, robustness and efficiency of the proposed approach for oscillation mode estimation.

Estimating electromechanical oscillation modes from synchrophasor measurements in China Southern Power Grid

Frequency stability-based controlled islanding scheme based on clustering algorithm and electrical distance using real-time dynamic criteria from WAMS data

Dominant inter-area oscillation mode identification using local measurement and modal energy for large-scale power systems with high grid-tied VSCs penetration

Damping inter-area oscillation by generation rescheduling based on wide-area measurement information

Power system monitoring and control in real-time is a challenging task for modern power system due to large number of operational constraints involved. This paper proposes a method to find the real-time transient stability state and identification of the coherent generator groups by predicting the rotor angle values following a large disturbance through radial basis function neural network. The first six cycle data of rotor angles and voltages of generators from fault clearing obtained through synchrophasor measurements are taken as the input to the neural network. The proposed method is also able to determine synchronism state of the individual machine in real-time. The proposed scheme is investigated on IEEE-39 bus test system to show the effectiveness of the proposed scheme.

The ancient masonry city hall in L'Aquila, Italy (Margherita Palace) that was severely damaged during the L'Aquila earthquake in April 2009 has been subjected to ambient vibration tests to determine its dynamic characteristics such as the natural frequencies, mode shapes, and damping ratios. The processing of ambient vibration responses is performed with three output-only identification approaches: (1) the frequency-domain decomposition, (2) random decrement (RD) technique combined with the eigensystem realization algorithm (ERA), and (3) the natural excitation technique (NExT) combined with ERA. Measurements were made in the palace and in the civic tower using 15 sensors in three different configurations, and the ambient vibration tests involved the simultaneous measurements of longitudinal and lateral vibrations. Lateral vibration modes for the civic tower were identified clearly; a total of five modal shapes and frequencies were identified in the frequency range 0 to 7 Hz. The modal identification in the palace was more difficult because of the severe damage; however, the two bending modes in the perpendicular directions were identified. The comparison of the three experimental techniques shows good agreement, confirming the reliability of the three identification methods. The results of the modal identification have been used to update the finite-element model of the palace. DOI: 10.1061/(ASCE)ST.1943-541X.0000494. © 2012 American Society of Civil Engineers. CE Database subject headings: Historic buildings; Masonry; Finite element method; Vibration; Structural analysis; Earthquakes; Italy. Author keywords: Output-only modal identification; Frequency domain decomposition (FDD); Eigensystem realization algorithm (ERA); Random decrement (RD); Masonry structure; Finite-element model; Ambient vibration; Identification; EMA; Operational modal analysis.

In recent year, renewable energy sources like wind and solar photovoltaic (PV) are rapidly evolving worldwide. Accordingly, fossil energy based conventional generators are being replaced by renewable energy sources for powering electric production in smart grid. Some low inertia issues will be brought in future smart grid and will affect system frequency stability. An estimation method of dominant electromechanical oscillation (EMO) mode based on synchrophasor data of low inertia power system is proposed. A power system with a lot of renewable energy sources tends to have low inertia power system, an accurate estimated model is difficult to achieve by existing methods, i.e., Simplified Oscillation mode (SOM) and Prony methods, because of high fluctuation of system frequency. The proposed method is developed by observing decaying signal period. Then, a dominant oscillation mode is evaluated based on the proposed method to obtain dominant frequency and damping factor. Finally, the effectiveness of proposed method is examined via traditional and low inertia power systems based on simulated synchrophasor data of a two-area smart grid, and comparisons between proposed method and existing method is also discussed.

One of the main characteristics of Smart Grids is self healing. This means that the grid should rapidly detects, analyzes, acts and restores for system instabilities. Controlled islanding is one of the techniques used to mitigate these system instabilities. In order to split the system into islands, groups of coherent generators should be firstly identified. This paper proposes a k-means technique of the Matlab Bioinformatics toolbox, which utilizes phasor measurement units (PMUs) outputs for identifying the generators coherent groups. The Bioinformatics technique is used for real time clustering of generators, which swing together, enabling splitting the system into islands much faster. The proposed technique is applied firstly on the IEEE -39 bus system. Then it is applied to an actual grid (the Egyptian Power 218 bus system) as study systems. Compared to conventional coherency approaches, the bioinformatics technique is much simple in implementation and supports real time applications.

A power system's inter-area electromechanical modes of oscillation determine if the system is small-signal stable. Mode meters are tools that estimate the modes based on ambient synchrophasor measurements, allowing continuous monitoring of the system's stability margin. This paper reports on the application of mode meter algorithms to 21 months of synchrophasor measurements from the United States Eastern Interconnection (EI). Estimates for three modes were updated every minute, providing a much more comprehensive set of results than previous studies based on disturbance recordings. The detailed signal designs and mode meter configurations provided in this paper can be used to accelerate mode meter deployments in the El. Analysis of the mode estimates reveal for the first time how seasonal load changes and system reconfiguration impact the El system's dynamics.

This work is placed on the scope of characterization and modeling of structures and the central objective is to establish a computer code capable to provide models for structures of interest from practical experimentation, to be used to refine the computer models a posteriori. To realize that project, the experimental modal analysis was applied in the Eigensystem Realization Algorithm – ERA. As well as most of the techniques of identification of dynamic of systems, the ERA also creates the compute modes, being necessary the application of procedures for filtering the real modes before the noises and/or computers modes. In this work, two techniques for the distinction were used: Modal Amplitude Coherence (MAC) and Modal Singular Value (MSV). Beside this, a novel approach to help interpret and filter based in MSV deviation was proposed and tested, producing good results. To achieve this, a computer code that receives experimental data and returns the global properties of the system, i.e., the natural frequencies, the damping factors and the mode shapes was developed. The preliminary results, developed in the know structure, indicate the code has the potential to handle with complex geometries.

Multiple degree of freedom (MDOF) algorithms are the dominant methods for extracting modal parameters from measured data. These methods are founded on the notion that because the response of a linear dynamic system is the sum of many modal contributions, the extraction technique must deal with all of the modal parameters in a simultaneous fashion. The Mode Isolation Algorithm (MIA) described here is a frequency domain formulation that takes an alternative viewpoint. It extracts the modal parameters of each mode in an iterative search, and then refines the estimation of each mode by isolating its effect from the other modal contributions. The first iteration estimates modes in a hierarchy of their dominance. As each mode is estimated, its contribution is subtracted from the data set, until all that remains is noise. The second and subsequent iterations subtract the current estimates for all other modes to identify the properties of the mode under consideration. The various operations are described in detail, and then illustrated using data from a four-degree-of-freedom system that was previously used to assess the Eigensystem Realization Algorithm (ERA) and Enhanced ERA. Eigenvalues and mode shapes are compared for each algorithm. Another example analyzes simulated data for a cantilever beam with three suspended one-degree-of-freedom subsystems, in which the parameters are adjusted to bring two natural frequencies into close proximity. The results suggest that MIA is more accurate, and more robust in the treatment of noisy data, than either ERA version, and that it is able to identify modes whose bandwidth is comparable to the difference of adjacent natural frequencies.

Since phasor measurement units (PMU) were invented, there has been growing interest in developing methodologies for improving monitoring, protection and control of power systems in real time. In this study, the authors propose a new methodology, based on graph modelling, to identify coherent groups of generators in a real‐time fashion. The coherent groups are identified with instantaneous values measured from the system through PMUs, and the methodology needs neither setting the number of desired groups nor defining a threshold value since it is based on coupling factors between generators. Moreover, it is proposed a new method for the online definition of areas for islanding when this action is required as the latest emergency control method. The methodology assigns the non‐generation buses to the previously found generators coherent groups considering three criteria: electrical distance to the group of generators, topology, and operational constraints, which are verified by mean of an optimal power flow. The methodology is tested on the IEEE 39‐bus and IEEE 118‐bus test systems. Results show that the real‐time identification of coherent groups and the definition of areas allow the development of islanding strategies with promising results.

This paper presents a new coherency identification method for dynamic reduction of a power system. To achieve dynamic reduction, coherency-based equivalence techniques divide generators into groups according to coherency, and then aggregate them. In order to minimize the changes in the dynamic response of the reduced equivalent system, coherency identification of the generators should be clearly defined. The objective of the proposed coherency identification method is to determine the optimal coherent groups of generators with respect to the dynamic response, using the Partitioning Around Medoids (PAM) algorithm. For this purpose, the coherency between generators is first evaluated from the dynamic simulation time response, and in the proposed method this result is then used to define a dissimilarity index. Based on the PAM algorithm, the coherent generator groups are then determined so that the sum of the index in each group is minimized. This approach ensures that the dynamic characteristics of the original system are preserved, by providing the optimized coherency identification. To validate the effectiveness of the technique, simulated cases with an IEEE 39-bus test system are evaluated using PSS/E. The proposed method is compared with an existing coherency identification method, which uses the K-means algorithm, and is found to provide a better estimate of the original system.