Generator Coherency Using the Wavelet Phase Difference Approach

Abstract

In this paper, the wavelet phase difference (WPD) approach is applied for the identification of power system areas with coherent generator groups. This approach allows observation, at different frequency bands, of movement of low frequency electromechanical oscillations (LFEO), identified at different parts of the power system and the identification of the inter-area components that move or do not move together. An illustration of the applied approach was performed on the New England (NE) 39-bus test system. The interesting results of WPD application are also presented in a real wide-area measurement data from European interconnected power system. By using the discrete wavelet transform (DWT) and Hilbert-Huang transform (HHT), the validation of results from WPD approach is also given.Index Terms-Discrete wavelet transform, generator coherency, Hilbert-Huang transform, power system, wavelet phase difference.

A Coherency Identification Method of Active Frequency Response Control Based on Support Vector Clustering for Bulk Power System

“…Among them, when V o = 0.0650, the optimal clustering result can be obtained for the AFR control (as shown in Figure 8), and the number of the coherent groups is six. 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29} 0.0767 4 {1}, {2, 3,4,5,6,7,8,9,10,12,13,14,15,17,18,19,20,21,22,24,25,26,27,28 When the number of the coherent groups of the generator buses on the 500-kV grid structure of the Henan power grid is six, the AFR control effects with and without the clustering control are shown in Figure 9. As demonstrated in the simulation results, under the AFR control, if the clustering control is not adopted, the frequency nadir and the transient maximum frequency of the Henan power grid are 49.5093 Hz and 50.2918 Hz, respectively.…”

“…This provides a new way for the coherency identification of generators after disturbance. The existing methods based on the PMU measured data include the independent component analysis approach [14], the improved Laplacian eigenmap algorithm [15], the principal component analysis method [16], the graph theory method [17], the wavelet phase difference approach [18], and the dynamic coherency determination method [19], etc. These methods are all for the transient stability of a power system in which the clearance speed of faults is very fast (from milliseconds to seconds).…”

Assessment of Transient Stability through Coherent Machine Identification by Using Least-Square Support Vector Machine

“…Techniques that are placed in the second type are based on disturbances and use TDS to find coherent groups of generators. For example, several studies have used the rotor trajectory index (RTI) [12], Fourier spectrum [19], or fast Fourier dominant interarea mode [20], principal component analysis [21], independent component analysis [22], hierarchical clustering methods [23,24], fuzzy c-medoids algorithm [25,26], wavelet [27], and Hilbert-Huang transform [28] to find coherent generators. Several techniques proposed in literature cannot always be reliable for all applications, such as system protection schemes or remedial action schemes, dynamic equivalencing, and controlled islanding.…”

A phase‐plane‐based dynamic coherency real‐time identification scheme for controlled islanding

“…E-mail: masa_hojo@tokushima-u.ac.jp *State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710049, China **Department of Electrical and Electronic Engineering, Tokushima University, 2-1 Minami-josanjima Tokushima 770-8506, Japan of generator coherency under different disturbances, which is, however, inconsistent with practical cases. The measurement-based methods identify the generator coherency based on the hierarchical clustering method [11], independent component analysis [12,13], spectral clustering method [14,15], wavelet phase difference [16], robust principal component analysis [17], Koopman mode analysis [18], graph theory [19,20], and intelligent method [21,22]. These methods can adapt to various operation conditions, topology changes, and different disturbances by using the real-time measurement data because all the influences of these factors are reflected in the transient response of the power system.…”