Low-frequency Electromechanical Modes Research Articles

Separating low-frequency electromechanical modes from spurious modes in ambient data analysis by means of modal stabilization diagrams

The separation of low-frequency electromechanical modes from spurious modes is a challenging problem in ambient data analysis performed by identification and signal processing techniques. In order to accurately extract all relevant dynamic properties from power system ambient measurement data, a filtering pre-processing step and high-order models are usually adopted during the identification process for an effective handling of noise, which may lead to the appearance of the so-called spurious modes into the identified system model. These spurious modes have no physical relevance, but they may present similar characteristics to the low-frequency electromechanical modes, affecting the accurate evaluation of the latter ones. Hence, this paper proposes a post-processing technique based on modal stabilization diagrams to distinguish low-frequency electromechanical modes and spurious modes from ambient data analysis. This proposed post-processing technique is applied in simulated and real measured ambient data with the purpose of estimating the dynamic properties of inter-area modes in large interconnected generation and transmission power systems (IPSs), as well as, global and local modes in active distribution networks (ADNs) with distributed generation (DG) based on synchronous generators.

Small signal stability criteria for descriptor form power network model

ABSTRACT This work considers the problem of small signal stability of a lossless power network by representing its mathematical model in descriptor form. A stability criterion is derived by constructing two orthogonal projectors corresponding to the system model. These projectors are then used in (i) constructing a Lyapunov function and (ii) deriving another criterion to identify the presence of poles (finite) of the system to the right of a given vertical line in the complex plane. The latter criterion is required to analyse whether the dominant low-frequency electro-mechanical modes, that cause oscillatory instability in the power network, settle down within a specified time limit. Using the derived criteria, linear matrix inequality feasibility problems are formulated to analyse small signal instability and oscillatory instability. Power network examples, including an IEEE benchmark system, are considered to demonstrate the developed approach.

Identification of Electromechanical Modes Based on the Digital Taylor-Fourier Transform

The digital Taylor-Fourier transform (DTFT) is used to identify low-frequency electromechanical modes in power systems. The identification process is based on the time-frequency analysis of nonlinear signals that arise after a large disturbance. The DTFT creates a signal decomposition, from which mono-component signals are extracted by spectral analysis using a filter bank. This analysis is accomplished through sliding-window data, which is updated each sample, yielding estimates of the reconstructed signal and providing information of its instantaneous damping and frequency. Results demonstrate the applicability of the proposition.

Adaptive tracking of system oscillatory modes using an extended RLS algorithm

The study of low-frequency electromechanical modes in power systems has experienced much progress in the past few years. In this research, a nonstationary recursive least-squares (RLS) algorithm with variable forgetting factor is combined with a Kalman filter to simultaneously estimate low-frequency electromechanical modes from measured ambient power system data. Extensions and generalizations to current adaptive filtering algorithms to account for nonstationarity are implemented and tested and the correspondence between the Kalman and RLS variables is examined.Applications of the proposed nonstationary RLS algorithm to track the evolving dynamics of critical power system electromechanical modes in both, simulated and measured data, are presented. Comparison with other RLS and least-mean squares algorithms demonstrate the accuracy of the proposed framework in tracking changes in modal parameters over time. The issues of computational efficiency and memory requirements are discussed in detail.

Electromechanical Mode Estimation Using Recursive Adaptive Stochastic Subspace Identification

Measurement based algorithms for estimating low-frequency electromechanical modes serve as useful practical methods to monitor the modal properties of power system oscillations in real-time. This paper proposes a recursive adaptive stochastic subspace identification (RASSI) algorithm for online monitoring of power system modes using wide-area synchrophasor data. The proposed method gives an online estimation of mode frequency and damping ratio as well as mode shapes using multi-channel measurement data. It exploits both the accuracy of subspace identification and fast computational capability of recursive methods. An adaptive method is proposed to enable fast tracking of modal evolution under poorly damped conditions together with low estimation variance under quasi-steady-state conditions. The algorithms are tested using simulated data from Kundur two-area test power system as well as measured data from real systems.

Use of arma block processing for estimating stationary low-frequency electromechanical modes of power systems

Use of arma block processing for estimating stationary low-frequency electromechanical modes of power systems

Use of ARMA Block Processing for Estimating Stationary Low-Frequency Electromechanical Modes of Power Systems

Accurate knowledge of low-frequency electromechanical modes in power systems gives vital information about the stability of the system. Current techniques for estimating electromechanical modes are computationally intensive and rely on complex system models. This research complements model-based approaches and uses measurement-based techniques. This paper discusses the development of an auto-regressive moving average (ARMA) block processing technique to estimate these low-frequency electromechanical modes from measured ambient power system data without requiring a disturbance. This technique is applied to simulated data containing a stationary low-frequency mode generated from a 19-machine test model. The frequency and damping factor of the estimated modes are compared with the actual modes for various block sizes. This technique is also applied to 35-minute blocks of actual ambient power system data before and after a disturbance and compared to results from Prony analysis on the ringdown from the disturbance.