Generator parameter validation and calibration process based on PMU data

Nayak, Neeraj; Chen, Heng; Schmus, Wayne R.; Quint, Ryan

doi:10.1109/tdc.2016.7519886

Cited by 17 publications

(10 citation statements)

References 8 publications

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“…When the input of the LTV system (7) of which the state matrix has a constant limit as stated in Assumption 1 can be accurately known, bounds of the state of the LTV system can be obtained by decomposing the LTV system into a linear time-invariant (LTI) system of which the state matrix is A(∞) as in Assumption 1 and an additional term. The result is stated as follows.…”

Section: Bounds Of Ltv Systems With Known Inputmentioning

confidence: 99%

“…Theorem 2. Consider an LTV system (7) with Assumption 1. Also, assume that u(t ) is a vector of which every entry is a continuous function.…”

Section: Bounds Of Ltv Systems With Unknown But Bounded Inputmentioning

confidence: 99%

“…Model calibration to reduce the consequences of model mismatch has been a hot research topic over the last decade. Many methods have been explored [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. The latest results show that most existing approaches can handle parameter errors well but model structure errors are very difficult to calibrate.…”

Section: Importance Of Quantifying Model Gap Of Synchronous Of Generatormentioning

confidence: 99%

“…Synchronous generator model calibration has been a hot research topic over the last decade in power systems research, with many approaches developed, for example, [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. Model mismatches are caused by either imprecise parameters or incomplete structural information, for example, unmodelled dynamics.…”

Section: Introductionmentioning

confidence: 99%

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Bounds of linear time‐varying systems with applications to quantify model gap of synchronous generators

Wang

Huang

2022

IET Control Theory & Appl

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Linear time-varying (LTV) systems play a key role in many engineering problems, for example, trajectory sensitivity analysis of power systems. Because LTV systems do not generally have closed-form solutions, estimates of their bounds are crucial to their analysis. In this paper, bounds for LTV systems with known inputs and unknown but bounded inputs are obtained via Grönwall's inequality. The estimated bounds are then used to quantify the gap between synchronous generators and their models in different scenarios through simulation.

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Section: Bounds Of Ltv Systems With Known Inputmentioning

confidence: 99%

“…Theorem 2. Consider an LTV system (7) with Assumption 1. Also, assume that u(t ) is a vector of which every entry is a continuous function.…”

Section: Bounds Of Ltv Systems With Unknown But Bounded Inputmentioning

confidence: 99%

Section: Importance Of Quantifying Model Gap Of Synchronous Of Generatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bounds of linear time‐varying systems with applications to quantify model gap of synchronous generators

Wang

Huang

2022

IET Control Theory & Appl

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“…1. Generator dynamic model validation using play-in PMU measurements generator dynamic modele using play-in PMU measurements, such as power plant model validation (PPMV) tool by Bonneville Power Administration (BPA) and Pacific Northwest National Laboratory (PNNL) [13], generator parameter validation (GPV) tool by Electric Power Group [14] and Power Plant Model Verification (PPMVer) tool by ISO-New England [7]. Currently, the model validation in most of the available tools is carried out either by visually inspecting the difference between the simulated and actual measurements of real and reactive power [7] or using some simple metric such as root-mean square error used in [13], peak value and peak-time of the first swing and steady-state error used in [15].…”

Section: Introductionmentioning

confidence: 99%

Advanced Performance Metrics and Sensitivity Analysis for Model Validation and Calibration

Agrawal¹,

Etingov²,

Huang³

2020

Preprint

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<pre>High quality generator dynamic models are critical to reliable and accurate power systems studies and planning. With the availability of PMU measurements, measurement-based approach for model validation has gained significant prominence. Currently, the model validation results are analyzed by visually comparing real--world PMU measurements with the model-based response measurements, and parameter adjustments rely mostly on engineering experience. This paper proposes advanced performance metrics to systematically quantify the generator dynamic model validation results by separately taking into consideration slow governor response and comparatively fast oscillatory response. The performance metric for governor response is based on the step response characteristics of a system and the metric for oscillatory response is based on the response of generator to each system mode calculated using modal analysis. The proposed metrics in this paper is aimed at providing critical information to help with the selection of parameters to be tuned for model calibration by performing enhanced sensitivity analysis, and also help with rule-based model calibration. Results obtained using both simulated and real-world measurements validate the effectiveness of the proposed performance metrics and sensitivity analysis for model validation and calibration.</pre>

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A new approach for system‐wide power system frequency model validation via measurement data

Sajjadi

Seifi

Delkhosh

2021

Engineering Reports

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Models are commonly used for various power system studies. This article presents a new framework to estimate and validate the parameters of the turbine-governor and also the load model in a multi-machine power system environment. The proposed method utilizes the center of inertia frequency and generating unit signals (especially active power) recorded by measurement devices to validate the parameters. The objective function is to minimize the total squared error between the simulated and the measured responses for some events. Particle swarm optimization is used to estimate the parameters. The accuracy of the estimated parameters is investigated through sensitivity analysis. Moreover, in order to improve the efficiency of the identification procedure for large-scale networks, a new scheme is proposed, which is based on the impact of each estimated parameter during the simulation time frames. The simulation results on the IEEE 9-bus and IEEE 118-bus systems show the validity and benefit of the proposed method.

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Generator parameter validation and calibration process based on PMU data

Cited by 17 publications

References 8 publications

Bounds of linear time‐varying systems with applications to quantify model gap of synchronous generators

Bounds of linear time‐varying systems with applications to quantify model gap of synchronous generators

Advanced Performance Metrics and Sensitivity Analysis for Model Validation and Calibration

A new approach for system‐wide power system frequency model validation via measurement data

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