Wavelet-Based Event Detection Method Using PMU Data

Kim, Do-In; Chun, Tae Yoon; Yoon, Sung-Hwa; Lee, Gyul; Shin, Yong-June

doi:10.1109/tsg.2015.2478421

Cited by 123 publications

(48 citation statements)

References 30 publications

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“…Voltage and frequency exhibit local characteristics such as different levels of voltage drop, oscillation, and frequency response. This uncorrelated signature of PMU data from a wide area contains information that is important for further PMU applications, such as system-operation decision [14], event detection/identification [15,16], fault location [17], monitoring/control of renewable resources [18], and stability analysis [19,20].…”

Section: Characteristics Of Real-world Pmu Datamentioning

confidence: 99%

“…As described in Figure 2, compression intervals are selected according to power-system conditions, ambient and event. This event-detection method utilizes an index of modified wavelet energy that was developed in Reference [15]. In this work, the average MWE value (AMWE) was adopted for all monitoring of wide-area power systems.…”

Section: Event Detection and Compression Interval Selectionmentioning

confidence: 99%

“…The importance of selecting compression interval considering event detection was discussed in References [9,11,22] because abnormal variation caused by an event can yield significant distortion in dimensionality reduction. In this paper, event detection is implemented using modified wavelet energy (MWE), proposed in Reference [15]. In ambient periods, long-term PMU data are collected for ambient dataset.…”

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confidence: 99%

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Multiscale PMU Data Compression via Density-Based WAMS Clustering Analysis

Lee

Kim

et al. 2019

Energies

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This paper presents a multiscale phasor measurement unit (PMU) data-compression method based on clustering analysis of wide-area power systems. PMU data collected from wide-area power systems involve local characteristics that are significant risk factors when applying dimensionality-reduction-based data compression. Therefore, density-based spatial clustering of applications with noise (DBSCAN) is proposed for the preconditioning of PMU data, except for bad data and the automatic segmentation of correlated local datasets. Clustered PMU datasets of a local area are then compressed using multiscale principal component analysis (MSPCA). When applying MSPCA, each PMU signal is decomposed into frequency sub-bands using wavelet decomposition, approximation matrix, and detail matrices. The detail matrices in high-frequency sub-bands are compressed by using a PCA-based linear-dimensionality reduction process. The effectiveness of DBSCAN for data compression is verified by application of the proposed technique to the real-world PMU voltage and frequency data. In addition, comparisons are made with existing compression techniques in wide-area power systems.

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Section: Characteristics Of Real-world Pmu Datamentioning

confidence: 99%

Section: Event Detection and Compression Interval Selectionmentioning

confidence: 99%

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confidence: 99%

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Multiscale PMU Data Compression via Density-Based WAMS Clustering Analysis

Lee

Kim

et al. 2019

Energies

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“…Event identification is based on analysis of the transient phenomena and their fluctuations in PMU signals including voltage, current, and frequency [10], [11]. However, as the PMU represents the phasor of the transient state besides the steady state, the nonlinearity and nonstationarity of the signal characteristics are relatively high to predict fluctuation and apply model-based analysis techniques [12]. For the realization of event-based situational awareness, the data-driven method is a suitable approach for the complex power systems under a transient condition, because it directly extracts the information from the event signals database.…”

Section: Introductionmentioning

confidence: 99%

Data Driven Method for Event Classification via Regional Segmentation of Power Systems

2020

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This paper presents a data-driven approach for event classification via a regional segmentation of power systems. The data-driven approach is suitable for the complex power systems under transient conditions, as it directly derives the information from the measurement signal database instead of modeling transient phenomena. However, measurement conditions of real-world power system will have unavoidable missing and bad data. Thus, it is necessary for data-driven model to have a robustness and adaptability about varying environment as well as system configurations and measurement conditions. In this work, the clustering-based regional segmentation of power systems is adopted to improve robustness of the data driven model by maintaining the fixed-input-feature format under varieties of measurement conditions. The clustering technique is applied to electrical buses for regional segmentation, and proposed features of phasor measurement unit (PMU) signals are extracted by integrating PMUs in each region based on wavelet analysis. As a result, the regional segmentation achieves improvement of data driven method for event classification with reduced number of calculations and management of bad data. Finally, we verify the event classification algorithm through a case study and analyze the performance of the algorithm for noise and computation time in addition to classification accuracy. INDEX TERMS Synchrophasor, phasor measurement unit (PMU), event classification, clustering, wavelet analysis, characteristic ellipsoid, convolutional neural network (CNN).

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“…These include Wind Energy Conversion Systems (WECSs) [1], [2], Solar Photo-Voltaic Generations (SPVGs) [3], [4], Energy Storage Systems (ESSs) [5], [6], High-Voltage Direct Current (HVDC) systems [7], [8], and Thermostatically Controlled Loads (TCLs) [9], [10], among others. In practice, the frequency signal used as input of the controller can be provided by the Phase-Locked Loop (PLL) needed to synchronize the converter with the grid [11], [12], or by Phasor Measurement Units (PMUs) connected at the point of coupling with the grid [13], [14]. However, from the simulation point of view, accurately estimating the local frequency to be regulated is still an open and urgent problem.…”

Section: Introductionmentioning

confidence: 99%

Hardware-in-the-Loop Validation of the Frequency Divider Formula

Ortega

Musa

Monti

et al. 2018

2018 IEEE Power &Amp; Energy Society General Meeting (PESGM)

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This paper validates a theoretical approach, namely, the frequency divider formula, recently proposed by the first and fourth authors to estimate local frequency variations based on the synchronous machine rotor speeds and on signals from phasor measurement units (PMUs). The validation is based on simulations performed in a Real-Time Digital Simulator with physical PMUs connected in the loop. The case study considers the well-known WSCC 3-machine, 9-bus test system. Simulation results show the high accuracy of the frequency divider formula to estimate the frequency at every bus of the network. Results also show that the frequency divider prevents the numerical issues due to fast variations of the voltage when measured by the PMU and indicate that such a formula can be utilized to test the fidelity of PMU implementations.

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Wavelet-Based Event Detection Method Using PMU Data

Cited by 123 publications

References 30 publications

Multiscale PMU Data Compression via Density-Based WAMS Clustering Analysis

Multiscale PMU Data Compression via Density-Based WAMS Clustering Analysis

Data Driven Method for Event Classification via Regional Segmentation of Power Systems

Hardware-in-the-Loop Validation of the Frequency Divider Formula

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