Indices for Automated Identification of Questionable Generator Models Using Synchrophasors

This letter proposes a new approach for online detection of low-quality synchrophasor data under both normal and event conditions. The proposed approach utilizes the features of synchrophasor data in time and frequency domains to distinguish multiple regional PMU signals and detect low-quality synchrophasor data. The proposed approach does not require any offline study and it is more effective to detect low-quality data with apparently indistinguishable profiles. Case studies from recorded synchrophasor measurements verify the effectiveness of the proposed approach.

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“…For a given frequency range with m frequency values, Frequency Phase Similarity [6], denoted by I f ps , is:…”

Section: Frequency Phase Similaritymentioning

confidence: 99%

“…λ is given as 10, and is set as 0.5. They are the same with [6]. The frequency range is [0, 5] Hz and higher frequency band is not considered.…”

Section: Case Studiesmentioning

confidence: 99%

Online Detection of Low-Quality Synchrophasor Data Considering Frequency Similarity

Silva-Saravia

Nayak

et al. 2021

IEEE Trans. Power Delivery

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“…The measurements, typically taken 30 times a second, can quickly track system changes undetectable through traditional monitoring systems used in the industry [1]. This makes new energy management applications possible, including model validation [2], dynamic state estimation [3], oscillation monitoring, islanding detection and wide area monitoring and control [4].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Area Angle Monitoring Using Synchrophasors: A Practical Framework and Utility Deployment

Dobson

Martin

et al. 2021

IEEE Trans. Smart Grid

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This paper develops a practical framework of Area Angle Monitoring (AAM) to monitor in real time the stress of bulk power transfer across an area of a power transmission system. Area angle is calculated from synchrophasor measurements in real time to provide alert to system operators if the area angle exceeds pre-defined thresholds. This paper proposes a general method to identify the warning threshold of area angle and a simplified method to quickly update area angle thresholds under significant topology change. A mitigation strategy to relieve the area stress is also proposed. In order to handle the limited coverage of synchrophasor measurements, this paper proposes a method to estimate phase angles for boundary buses without synchrophasor measurements, which extends the application scenario of AAM. AAM is verified for a power transmission area in the Western Electricity Coordinating Council system with both simulated data and synchrophasor measurements recorded from real events. A utility deployment for real-time application of AAM with livestream and recorded synchrophasor data is described.

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“…Several tools have been developed to validate 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]. Current practices of analyzing model validation results include visually comparing actual generator real and reactive power response measured using PMUs with the simulated model-based response corresponding to a large system event [7], [15]. Some examples of metrics that have been proposed for quantifying mismatch between actual and simulated generator response include root-mean square error (RMSE) used in [13], peak value and peak-time of the first swing and steady-state error used in [16], normalized RMSE metric proposed in [15], [17].…”

Section: Introductionmentioning

confidence: 99%

“…Current practices of analyzing model validation results include visually comparing actual generator real and reactive power response measured using PMUs with the simulated model-based response corresponding to a large system event [7], [15]. Some examples of metrics that have been proposed for quantifying mismatch between actual and simulated generator response include root-mean square error (RMSE) used in [13], peak value and peak-time of the first swing and steady-state error used in [16], normalized RMSE metric proposed in [15], [17]. Similar to the RMSE metric, the optimization function used for for model calibration in [18] defines waveform similarity metric that quantifies the mismatch between actual and modelbased response based on similarity of the curves.…”

Section: Introductionmentioning

confidence: 99%

Advanced Performance Metrics and Their Application to the Sensitivity Analysis for Model Validation and Calibration

Agrawal

Etingov

Huang

2021

IEEE Trans. Power Syst.

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High-quality generator dynamic models are critical to reliable and accurate power systems studies and planning. With the availability of PMUs, measurement-based approach for model validation has gained significant prominence. In this approach, the quality of a model is analyzed by visually comparing measured generator response with the model-based simulated response for large system disturbances. This paper proposes a new set of performance metrics to assess the model validation results to facilitate automation of the model validation process. In the proposed methodology, first, the slow governor response and comparatively faster oscillatory response are separated, and then a separate set of performance metrics is calculated for each of these two components. These proposed metrics quantify the mismatch between the actual and model-based response in a comprehensive manner without missing any information enabling automation of the process. Furthermore, in this paper, we are also proposing that the sensitivity analysis for model calibration be performed with respect to the proposed metrics for the systematic identification of key parameters. Results obtained using both simulated and real-world case-studies validate the effectiveness of the proposed performance metrics for model validation and their application to the sensitivity analysis for model calibration.

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Indices for Automated Identification of Questionable Generator Models Using Synchrophasors

Cited by 6 publications

References 2 publications

Online Detection of Low-Quality Synchrophasor Data Considering Frequency Similarity

Online Detection of Low-Quality Synchrophasor Data Considering Frequency Similarity

Real-Time Area Angle Monitoring Using Synchrophasors: A Practical Framework and Utility Deployment

Advanced Performance Metrics and Their Application to the Sensitivity Analysis for Model Validation and Calibration

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