Data-driven Identification and Prediction of Power System Dynamics Using Linear Operators

Sharma, Prolay; Huang, Bowen; Ajjarapu, V.; Vaidya, Umesh

doi:10.1109/pesgm40551.2019.8973724

Cited by 17 publications

(17 citation statements)

References 27 publications

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“…As pointed out in section I, majority of the data-driven approaches consider the availability of time-series information for all (or most) state variables (x(t)). Existing work on datadriven nonlinear dynamic characterization [24]- [33], either assumes that measurements for these states are available (as they can be generated in simulation studies); or else, a simple swing equation based 2 nd order dynamics are considered with δ i , ω i states and thus ignoring other higher order dynamics of system components.…”

Section: Need For Output Measurements Based Power Systems Dynamics Ch...mentioning

confidence: 99%

“…In particular, the Koopman operator provides a linear representation (not the linear approximation) for a nonlinear dynamical system. Recent work has shown that the linear operator based approach is suitable for stability studies [24], generator coherency identification [30], system inertia estimation [31], and PSS parameter tuning and controller design [32] and trajectory prediction [33]. One of the key assumptions in these works [24]- [33] is the availability of state measurements, i.e., it is assumed that time-series measurements are available for all dynamic states of a given system.…”

Section: Introductionmentioning

confidence: 99%

“…Recent work has shown that the linear operator based approach is suitable for stability studies [24], generator coherency identification [30], system inertia estimation [31], and PSS parameter tuning and controller design [32] and trajectory prediction [33]. One of the key assumptions in these works [24]- [33] is the availability of state measurements, i.e., it is assumed that time-series measurements are available for all dynamic states of a given system. Though analytically justified, this assumption acts as a roadblock for implementation of these methods for a real power system.…”

Section: Introductionmentioning

confidence: 99%

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Data-driven Identification of Nonlinear Power System Dynamics Using Output-only Measurements

Ajjarapu¹,

Vaidya²

2021

Preprint

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In this paper, we propose a novel approach for the data-driven characterization of power system dynamics. The developed method of Extended Subspace Identification (ESI) is suitable for systems with output measurements when all the dynamics states are not observable. It is particularly applicable for power systems dynamic identification using Phasor Measurement Units (PMUs) measurements. As in the case of power systems, it is often expensive or impossible to measure all the internal dynamic states of system components such as generators, controllers and loads. PMU measurements capture voltages, currents, power injection and frequencies, which can be considered as the outputs of system dynamics. The ESI method is suitable for system identification, capturing nonlinear modes, computing participation factor of output measurements in system modes and identifying system parameters such as system inertia. The proposed method is suitable for measurements with a noise similar to realistic system measurements. The developed method addresses some of the known deficiencies of existing datadriven dynamic system characterization methods. The approach is validated for multiple network models and dynamic event scenarios with synthetic PMU measurements.

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Section: Need For Output Measurements Based Power Systems Dynamics Ch...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data-driven Identification of Nonlinear Power System Dynamics Using Output-only Measurements

Ajjarapu¹,

Vaidya²

2021

Preprint

Self Cite

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“…Power systems data analysis is the current frontier of innovation, exploration and productivity, and its research is on the rise. Such data analysis has addressed diverse system areas like coherency groups identification [7], trajectory prediction to identify system dynamics from a noisy measurements [8], short-horizon wind power forecasting [9], online prediction of transient stability with renewables [10], photo-voltaic power production nowcasting in microgrids [11], power system collapse prediction [12], wide area control [13], load frequency control [14], and for renewable integration impact assessment [15].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models

et al. 2021

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Frequency in power systems is a real-time information that shows the balance between generation and demand. Good system frequency observation is vital for system security and protection. This paper analyses the system frequency response following disturbances and proposes a data-driven approach for predicting it by using machine learning techniques like Nonlinear Auto-regressive (NAR) Neural Networks (NN) and Long Short Term Memory (LSTM) networks from simulated and measured Phasor Measurement Unit (PMU) data. The proposed method uses a horizon-window that reconstructs the frequency input time-series data in order to predict the frequency features such as Nadir. Simulated scenarios are based on the gradual inertia reduction by including non-synchronous generation into the Nordic 32 test system, whereas the PMU collected data is taken from different locations in the Nordic Power System (NPS). Several horizon-windows are experimented in order to observe an adequate margin of prediction. Scenarios considering noisy signals are also evaluated in order to provide a robustness index of predictability. Results show the proper performance of the method and the adequate level of prediction based on the Root Mean Squared Error (RMSE) index.

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“…This lifting can be approximated using data generated from the underlying nonlinear dynamics by the well-known Extended Dynamic Mode Decomposition (EDMD) algorithm [6]. These tools have been successfully applied in many domains such as fluid dynamics [7] and power systems [8,9], to understand the principle components/modes of given nonlinear dynamics [10]. Recently, Koopman theory has been introduced to the control synthesis tasks, in the hopes that a controller designed in the lifted space could be easier than that in the original state space.…”

Section: Introductionmentioning

confidence: 99%

A Convex Data-Driven Approach for Nonlinear Control Synthesis

2021

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We consider a class of nonlinear control synthesis problems where the underlying mathematical models are not explicitly known. We propose a data-driven approach to stabilize the systems when only sample trajectories of the dynamics are accessible. Our method is built on the density-function-based stability certificate that is the dual to the Lyapunov function for dynamic systems. Unlike Lyapunov-based methods, density functions lead to a convex formulation for a joint search of the control strategy and the stability certificate. This type of convex problem can be solved efficiently using the machinery of the sum of squares (SOS). For the data-driven part, we exploit the fact that the duality results in the stability theory can be understood through the lens of Perron–Frobenius and Koopman operators. This allows us to use data-driven methods to approximate these operators and combine them with the SOS techniques to establish a convex formulation of control synthesis. The efficacy of the proposed approach is demonstrated through several examples.

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Data-driven Identification and Prediction of Power System Dynamics Using Linear Operators

Cited by 17 publications

References 27 publications

Data-driven Identification of Nonlinear Power System Dynamics Using Output-only Measurements

Data-driven Identification of Nonlinear Power System Dynamics Using Output-only Measurements

Data-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models

A Convex Data-Driven Approach for Nonlinear Control Synthesis

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