Synchronized Measurement Technology Supported Online Generator Slow Coherency Identification and Adaptive Tracking

Abstract

In an electric power system, slow coherency can be applied to identify groups of the generating units, the rotors of which are swinging together against each other at approximately the same oscillatory frequencies of inter-area modes. This serves as a prerequisite-step of several emergency control schemes to identify power system control areas and improve transient stability. In this paper, slow coherent generators are grouped based on the direction and the strength of electromechanical coupling between different generators. The proposed algorithm performs low-pass filtering of generator frequency measurements. It adaptively determines the minimal number of the measurements to be processed in an observation window, and performs data selectivity to prevent mixing of interfering coherency indices. Finally, it adaptively tracks grouping changes of slow coherent generators and determines a finite number of groups for an improved affinity propagation clustering. The proposed algorithm is implemented as an online MATLAB program and verified in real-time using RTDS power system simulator with the integration of actual synchronized measurement technology components as hardware-in-the-loop. The obtained results demonstrate the effectiveness of the proposed algorithm for robust and near real-time identification of grouping changes of slow coherent generators during the quasi-steady-state and electromechanical transient period following a disturbance. where he was involved in modeling SF6 circuit breakers. His current research interests include future power systems, large-scale power system transients, intelligent protection for future power systems, and wide-area monitoring and protection. Prof. Popov is a member of CIGRE. He has actively participated in WG C4.502 and WG A2/C4.39. He was a recipient of the IEEE PES Prize Paper Award and the IEEE Switchgear Committee Award in 2011. He is an Associate Editor of the International Journal of Electric Power and Energy Systems. Mart A. M. M. van der Meijden (M'10) received the M.Sc. degree (cum laude) in electrical engineering from the Eindhoven University of Technology, Eindhoven, The Netherlands, in 1981.He is leading research programs on intelligent electrical power grids, reliable, and large-scale integration of renewable (wind and solar) energy sources in the European electrical power systems and advanced grid concepts. He has more than 30 years of working experience in the field of process automation and the transmission and the distribution of gas, district heating, and electricity. Since 2003, he has been with TenneT TSO, Arnhem, The Netherlands, Europe's first cross-border grid operator for electricity, where he is the Manager of Research and Development/Innovation and was responsible for the development of the TenneT long-term vision on the electrical transmission system. He has been a Full Professor (part-time) with the

Slow Coherency Identification and Power System Dynamic Model Reduction by Using Orthogonal Structure of Electromechanical Eigenvectors

“…As discussed in Section V-B, it is known that the NPCC 48-machine test system can be well decomposed into 9 areas based on its 9 slowest electromechanical modes. Our previous case study in Section V-B comes to the same conclusion, but it additionally identifies alternative area structures consisting of 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,33,34,35 , 36 2 32, 37, 38, 39, 40, 41, 42 3 43, 44, 45, 46, 47, 48 3 and 6 areas (see Figure 2). The present case study illustrates the validity of decomposing the NPCC system into 6 areas by using our grouping algorithm in Section IV.…”

“…The advantages of signalbased coherency approaches include high adaptation to the current operating condition and low dependence on system model data. However, the wide-area dynamic response signals are disturbance-dependent, and their processing poses several challenges including unreliable results during changing system conditions [10], sensitivity to spurious signal components [11], inconclusive signal similarities and data window lengths [10], clustering issues (e.g., choice of the number of groups) etc.…”

WAMS-Supported Power Mismatch Optimization for Secure Intentional Islanding

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