Performance Analysis of Data-Driven Techniques for Detection and Identification of Low Frequency Oscillations in Multimachine Power System

Abstract

In power systems, identification and damping of low-frequency oscillations(LFO) is very crucial to maintain the small signal stability. Hence the computation of eigenvalues, eigenmode shapes, participation factors, and coherency of generators are essential parameters of critical LFO modes. The existing data-driven approaches explore either one or two aspects of modal parameters from the dynamic pattern of the measurement data. In the present work, two approaches i) Iterative Approach(IA) ii) Non-Iterative Subspace(SS) method of data-driven techniques are used to estimate the state-space model of the system under study from the measurement data in a holistic framework. Based on the estimated system model, the eigenvalues of LFO, eigenmode shapes, participation factor, and coherency of associated generators participating in electromechanical oscillations are computed. Finally, from the estimated participation factors for the Inter-area oscillation mode (IAM), the Static Synchronous Compensator (STATCOM) damping controller is designed and placed at the generator with the highest participation factor for damping of inter-area oscillation. The enhancement of damping ratio of inter area mode with STATCOM damping controller is estimated and verified using IA & SS data driven approaches for the first time. In this work, IA uses prediction-error minimization algorithm (PEM) & Parallel computing techniques and SS method uses Multivariable Output Error State Space (MOESP) algorithm for the estimation of Hankel matrix from the measured data. The effectiveness of data-driven approaches are demonstrated by the simulation of a IEEE 4-machine,10-bus power system using MATLAB / Simulink. IA & SS methods incorporating wavelet based denoising techniques are very effective in identifying the LFO modes even with noisy measurement. The efficacy of the denoising to suppress the effect of noise is demonstrated by comparing with noiseless environment. The results of data-driven approaches indicate their high degree of accuracy and efficiency in being consistent with Eigenvalue analysis (EA) performed on the system.

Methodological Aspects of Industrial and Transport Noise Monitoring

“…The spectra of most industrial and transport noises contain low-frequency noise and high-level infrasound. The results of acoustic measurements show that if airborne noise levels are about 90-100 dBA, then the presence of infrasound with a SPL of 100-107 dB can be expected [15].…”

“…It should be noted that in addition to the equivalent sound level, when describing and characterizing short-term sounds or noises, in the international practice of acoustic measurements (not standardized in Russia), the exposure parameter of a separate noise phenomenon (event) is often used [15]. The equivalent sound exposure level is the level that, when fixed for a time interval of 1 s, produces sound energy in dBA that is identical to the energy of an actual transient sound or noise.…”

Online Tracking of Small-Signal Stability Rightmost Eigenvalue Based on Reference Point

“…6. The object of DTW algorithm is to find an optimal warping path (red squares) that goes across this matrix as ( 14): (14) where d j = (n, k) j represents the j-th element of the optimal warping path, the horizontal and vertical coordinates of d j represent the corresponding point of R and Q, respectively. Generally speaking, the constraint conditions mean that the warping path should subject to the boundary condition, continuity, and monotonicity.…”

“…Data-driven classification models are convenient to predict stability labels [12] [13], while such approaches may not provide the information on oscillation modes at current OPs, such as frequency and damping. To obtain more oscillation mode information, some studies have used data-driven method to generate the linearized state matrix and calculate the eigenvalues [14] [15]. However, such method still suffers from the burden of computation.…”

Multi‐objective precise phasor measurement locations to assess small‐signal stability using dingo optimizer

“…They cannot estimate closely spaced modes. Shetty et al 35 suggested the Fast Fourier Transform to only identify the natural frequency of the modes. A multi-resolution-based technique 36,37 can extract information on oscillatory modes, but its accuracy depends on the mother wavelet and decomposition levels.…”