Hilbert-Huang transform (HHT) is applied to wide area measurement system (WAMS) data in order to identify inter-area oscillations present in the system. HHT is used to extract instantaneous frequency of multicomponent signal using Empirical Mode Decomposition (EMD) and Hilbert transform. Upon identification of oscillatory modes, these oscillations are damped through power system stabilizers (PSS) connected to the exciters of the generators. Conventional PSS uses a local signal like generator speed deviation or real power output deviation. Here, two signals have been considered for PSS. The first signal is local signal that is the speed deviation of the generator at which PSS is connected. The second signal is a global signal. Several global signals have been tested. The global signals used are the remote rotor speed deviations, tie-line real power and voltage magnitude. The proposed method has been applied to two-area test system and the simulation results are presented.
A method is proposed to identify the generators participating in an inter-area mode and the coherent generators by analyzing the discrete signals from phasor measurement units (PMU). In the present work, Zolotarev polynomial based filter bank (ZPBFB) is adopted to decompose the measured signals into monocomponents for modal frequency and damping. ZPBFB is preferred due to the narrow bandwidth of 0.1 Hz and hence closely spaced modes can be distinguished. All the generator speed signals are analysed with ZPBFB to identify the generators participating in an inter-area mode. Principal component analysis (PCA) is widely used for clustering sampled data. It is proposed in the present work to apply PCA on the decomposed signals, obtained from ZPBFB, of those generators participating in an inter-area mode for generator coherency. The efficacy of the proposed method is demonstrated on IEEE two-area test system, 16-machine, 68-bus system and on real time signals recorded by wide area frequency measurement system (WAFMS) in India on November 30, 2011. The detailed simulation results are presented. The performance of the proposed method is compared with small signal stability analysis and wavelet phase difference (WPD) approach.
Controlled islanding is an effective way of preventing the system from catastrophic blackouts. This is generally solved either as a constrained combinatorial optimization problem or a slow coherency based linearized approach. The combinatorial explosion of the solution space of an extensive power network increases the complexity of solving, while the linearized slow coherency approach cannot track the varying coherent generator groups with a change in system operating conditions. Offline coherent studies are utilized in wide area measurement system (WAMS) data-based approaches to determine islanding boundaries. So, the present study proposes a novel coherency based controlled islanding technique that clusters generators and load buses simultaneously from the measured signals, ensuring generator coherency. Therefore, identification of inter-area modes from bus voltage angle signals is necessary to determine coherent bus groups. So, Zolotarev polynomial based filter bank (ZPBFB) is adopted in the present work to determine inter-area modes. The dimensional reduction techniques are used to cluster the coherent buses. The bus clusters thus obtained with the proposed method are compared with bus clusters determined from small signal stability analysis. The proposed method is demonstrated on IEEE 39-bus, 68-bus and 118-bus test systems and compared with graph spectra based controlled islanding.
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