Granger Causality for prediction in Dynamic Mode Decomposition: Application to power systems

Gunjal, Revati; Nayyer, Syed Shadab; Wagh, S. R.; Stankovic, A. M.; Singh, N. M.

doi:10.1016/j.epsr.2023.109865

Granger Causality for prediction in Dynamic Mode Decomposition: Application to power systems

Journal Article · Sat Sep 23 00:00:00 EDT 2023 · Electric Power Systems Research

DOI:https://doi.org/10.1016/j.epsr.2023.109865· OSTI ID:2405182

^[1]; Nayyer, Syed Shadab ^[2]; Wagh, S. R. ^[3]; Stankovic, A. M. ^[4]; Singh, N. M. ^[5]

Veermata Jijabai Technological Institute (VJTI), Mumbai, (India); SLAC
Veermata Jijabai Technological Institute (VJTI), Mumbai, (India); Motwane Manufacturing Company Private Limited, Nashik (India)
Veermata Jijabai Technological Institute (VJTI), Mumbai, (India); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)
SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Veermata Jijabai Technological Institute (VJTI), Mumbai, (India)
Veermata Jijabai Technological Institute (VJTI), Mumbai, (India)

Here, the dynamic mode decomposition (DMD) technique extracts the dominant modes characterizing the innate dynamical behavior of the system within the measurement data. For appropriate identification of dominant modes from the measurement data, the DMD algorithm necessitates ensuring the quality of the input measurement data sequences. On that account, for validating the usability of the dataset for the DMD algorithm, the paper proposed two conditions: Persistence of excitation (PE) and the Granger Causality Test (GCT). The virtual data sequences are designed with the hankel matrix representation such that the dimensions of the subspace spanning the essential system modes are increased with the addition of new state variables. The PE condition provides the lower bound for the trajectory length, and the GCT provides the order of the model. Satisfying the PE condition enables estimating an approximate linear model, but the predictability with the identified model is only assured with the temporal causation among data searched with GCT. The proposed methodology is validated with the application for coherency identification (CI) in a multi-machine power system (MMPS), an essential phenomenon in transient stability analysis. The significance of PE condition and GCT is demonstrated through various case studies implemented on 22 bus six generator system.

View Accepted Manuscript (DOE)

Research Organization:: SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC02-76SF00515

OSTI ID:: 2405182

Journal Information:: Electric Power Systems Research, Journal Name: Electric Power Systems Research Vol. 225; ISSN 0378-7796

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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24 POWER TRANSMISSION AND DISTRIBUTION
Coherency identification
Dynamic Mode Decomposition (DMD)
Granger causality
Hankel matrix
Persistence of Excitation (PE)

Granger Causality for prediction in Dynamic Mode Decomposition: Application to power systems

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