Real-time Coherency Identification using a Window-Size-Based Recursive Typicality Data Analysis

This work presents a data-driven analysis of minimal length necessary for coherency detection considering a recursive form of the typicality-based Data analysis (TDA). It proposes a methodology that encloses the observation of the variance of the typicality (τ ) to asses the minimal window length necessary to determine the coherent buses, where the properties of the TDA approach and the groups of buses are iteratively calculated at every new data point sampled. Once the variance of each group reaches a certain value, the minimal window length is determined. Besides, this method preserves the TDA characteristics of using exclusively measurements, not requiring pre-determination of number of groups, group centers or cut-off constants. The method is applied to the well know 2-area Kundur test system, allowing to corroborate its effectiveness and draw conclusions regarding minimal window length dependence on the slowest inter-area mode.

[1]  Pei Zhang,et al.  A New Unified Scheme for Controlled Power System Separation Using Synchronized Phasor Measurements , 2011, IEEE Transactions on Power Systems.

[2]  W. Marsden.  I and J , 2012 .

[3]  Joe H. Chow,et al.  Power System Coherency and Model Reduction , 2019, Power System Modeling, Computation, and Control.

[4]  Arturo Roman Messina,et al.  An investigation on the use of power system stabilizers for damping inter-area oscillations in longitudinal power systems , 1998 .

[5]  Plamen P. Angelov,et al.  A Generalized Methodology for Data Analysis , 2018, IEEE Transactions on Cybernetics.

[6]  Fushuan Wen,et al.  Wide-area coherency identification of generators in interconnected power systems with renewables , 2017 .

[7]  Reza Iravani,et al.  A Dynamic Coherency Identification Method Based on Frequency Deviation Signals , 2016, IEEE Transactions on Power Systems.

[8]  P. Angelov,et al.  Empirical Approach to Machine Learning , 2018, Studies in Computational Intelligence.

[9]  Babu Narayanan,et al.  POWER SYSTEM STABILITY AND CONTROL , 2015 .

[10]  Mart A. M. M. van der Meijden,et al.  Synchronized Measurement Technology Supported Online Generator Slow Coherency Identification and Adaptive Tracking , 2020, IEEE Transactions on Smart Grid.

[11]  Yi Ding,et al.  Data-Driven Coherency Identification for Generators Based on Spectral Clustering , 2018, IEEE Transactions on Industrial Informatics.

[12]  R. Sarpong,et al.  Bio-inspired synthesis of xishacorenes A, B, and C, and a new congener from fuscol† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c9sc02572c , 2019, Chemical science.

[13]  Yi Ding,et al.  Robust System Separation Strategy Considering Online Wide-Area Coherency Identification and Uncertainties of Renewable Energy Sources , 2020, IEEE Transactions on Power Systems.

[14]  Peter W. Sauer,et al.  Power System Dynamics and Stability , 1997 .

[15]  Mo-Shing Chen,et al.  Power system modeling , 1974 .

[16]  Joe H. Chow,et al.  Identification of coherent trajectories by modal characteristics and hierarchical agglomerative clustering , 2018 .

[17]  W. Hager,et al.  and s , 2019, Shallow Water Hydraulics.

[18]  Joe H. Chow,et al.  Power System Coherency Detection From Wide-Area Measurements by Typicality-Based Data Analysis , 2022, IEEE Transactions on Power Systems.

[19]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[20]  Joe H. Chow,et al.  Voltage Control Performance Evaluation Using Synchrophasor Data , 2020, Hawaii International Conference on System Sciences.

[21]  Yi Ding,et al.  WAMS-Based Coherency Detection for Situational Awareness in Power Systems With Renewables , 2018, IEEE Transactions on Power Systems.