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.
Highlights
The increased power transfer over the transmission network infuriates the power system to undergo low-frequency oscillations(LFO)
Damping of Inter area mode (IAM) using STATCOM: As per the results obtained in section [IV.B], it is observed that the bus near the G1 is the best place to locate the STATCOM Supplementary modulation controller to damp out the IAM
The tunable parameters for STATCOM supplementary modulation controller (SMC) are X1 which is the value of the tunable entity to synthesize the Thevenin's voltage and Kr is the reactive current modulation controller gain.Using Sequential Linear Programming as per [60][61], the value of the parameters are calculated with respect to the required decrement factor σ = −. 25 & σ = −. 5 are Kr = 46.29, X1 = 0.01029 and Kr = 46.29, X1 = 0.014 respectively to damp the IAM
Summary
The increased power transfer over the transmission network infuriates the power system to undergo low-frequency oscillations(LFO). The performance of proposed data-driven methods in estimating the modal parameters and its accuracy is found to be consistent with the EA approach in extracting dynamic characteristics such as participation factor of the generator, mode shapes, and coherency of the generator for particular eigenmode. Based on the participation factors STATCOM parameters are tuned to damp the IAM in Section IV.D. In Section IV.E & Section IV.F, the performance of proposed approaches are verified during noisy environment & noise detection and wavelet denoising techniques are discussed. [M1m M2m . . . . Mkm] Where MEA is mode shapes matrix associated with multiple low frequency oscillatory modes
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