An alternative approach to measure similarity between two deterministic transient signals

Shin, Kihong

doi:10.1016/j.jsv.2016.02.037

Cited by 38 publications

(16 citation statements)

References 14 publications

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“…superscript 'm' corresponds to estimates using actual PMU measurements and 's' corresponds to estimates using simulated signal, w i is the weight factor for each mode which is chosen to be the energy of each mode given by (10), p is the number of dominant modes selected out of n modes based on the energy of each mode as compared to that of the energy of the mode having the highest energy, and |.| denotes absolute value of the quantity. The metric for calculating similarity of the phase between actual and simulated measurements is given by:…”

Section: Metrics For Quantification Of Model Validation Resultsmentioning

confidence: 99%

“…In this step, selection of model order is carried out for both the signals by comparing pre-processed original and reconstructed signal. Also, dominant modes are distinguished from the spurious ones by calculating energy of mode estimates using (10). 3) Calculate the two metrics that quantifies the similarity of the damping ratio, frequency, initial amplitude, and phase of each estimated dominant mode between the actual and simulated measurements using (11) and (14).…”

Section: Metrics For Quantification Of Model Validation Resultsmentioning

confidence: 99%

“…However, the approach is only suitable for cases when the simulated and actual measurements follow a similar shape-pattern. The frequencydomain based metrics, first defined in [10] as Magnitudeshape similarity measure, was applied for quantifying model validation results in [11]. This metric finds a weighted average of the similarity measure of the magnitude and phase spectra in frequency domain.…”

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confidence: 99%

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Initial Results of Quantification of Model Validation Results Using Modal Analysis

Agrawal¹,

Etingov²,

Huang³

2020

Preprint

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<div>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 simulated data. This paper proposes metrics to quantify the generator dynamic model validation results based on the response of generators to each system mode, which includes both local and inter-area, using modal analysis approach. The metrics provide information on the inaccuracy associated with the model in terms of the characteristics of each mode. Initial results obtained using the real-world data validates the effectiveness of the proposed metrics. In this paper, modal analysis was carried out using Prony method.</div>

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Section: Metrics For Quantification Of Model Validation Resultsmentioning

confidence: 99%

Section: Metrics For Quantification Of Model Validation Resultsmentioning

confidence: 99%

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confidence: 99%

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Initial Results of Quantification of Model Validation Results Using Modal Analysis

Agrawal¹,

Etingov²,

Huang³

2020

Preprint

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show abstract

“…Ref. [16] describes the application of frequency-domain based metrics, first defined in [17] as Magnitude-shape similarity measure, for quantifying model validation results. This metric finds a weighted average of the similarity measure of the magnitude and phase spectra in frequency domain.…”

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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“…Similarity can be characterized as the contrast between two images, and the similarity measure is a numerical difference between two dissimilar images under comparison [2]. When the two images match up to the maximum similarity, the similarity degree between two signals is required to test the system and in order to make a decision [3]. Similarity measure methods can be classifiedd into: information theoretical techniques, and statistical techniques [4].…”

Section: Introductionmentioning

confidence: 99%

A hybrid image similarity measure based on a new combination of different similarity techniques

Hamza¹,

Dihin²,

Abdulameer³

2020

IJECE

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Image similarity is the degree of how two images are similar or dissimilar. It computes the similarity degree between the intensity patterns in images. A new image similarity measure named (HFEMM) is proposed in this paper. The HFEMM is composed of two phases. Phase 1, a modified histogram similarity measure (HSSIM) is merged with feature similarity measure (FSIM) to get a new measure called (HFM). In phase 2, the resulted (HFM) is merged with error measure (EMM) in order to get a new similarity measure, which is named (HFEMM). Different kindes of noises for example Gaussian, Uniform, and salt & ppepper noiser are used with the proposed methods. One of the human face databases (AT&T) is used in the experiments and random images are used as well. For the evaluation, the similarity percentage under peakk signal to noise ratio (PSNR) is usedd. To show the effectiveness of the proposed measure, a comparision anong different similar technique such as SSIM, HFM, EMM and HFEMM are considered. The proposed HFEMM achieved higher similarity result when PSNR was low compared to the other methods.

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An alternative approach to measure similarity between two deterministic transient signals

Cited by 38 publications

References 14 publications

Initial Results of Quantification of Model Validation Results Using Modal Analysis

Initial Results of Quantification of Model Validation Results Using Modal Analysis

Advanced Performance Metrics and Sensitivity Analysis for Model Validation and Calibration

A hybrid image similarity measure based on a new combination of different similarity techniques

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