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/pesgm.2017.8274161

Cited by 16 publications

(15 citation statements)

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“…The transformed coefficients having greater concentration of energy correspond to a particular event, while those having lowest amount of energy imply to normal condition of the grid. The detail study using wavelet decomposition of PMU data for event detection can be referred from [18].…”

Section: A Wavelet Coefficient (Wc)mentioning

confidence: 99%

“…1(b) & (c) respectively. The best mother wavelet and its level selection follow the study discussed in [18]. Using wavelet, each data segment is decomposed to levels of coefficients of details ( ) from which square-root of SK (SRSK) are computed.…”

Section: A Segment Processingmentioning

confidence: 99%

“…The PMU signals; frequency and voltage magnitude are sampled at 50 Hz. The events associated with real power disturbance primarily leads to transient in frequency and voltage magnitude signals while, reactive power affects the voltage magnitude (locally) only [18]. The PMU signals accompanied with events are shown in Fig.…”

Section: B Nordic Grid Pmu Datamentioning

confidence: 99%

“…Wavelet analysis is established technique for non-stationary signals [17], however require appropriate sampling rate. Authors [18] have applied wavelet technique on discrete samples of collected PMU data followed by normalization based on the energy of the transformed coefficients. The energy content of decomposed wavelet coefficients was normalized against the decomposed coefficients of 1 hour PMU data samples.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Event Detection and Its Signal Characterization in PMU Data Stream

Negi¹,

Kishor

Uhlen

et al. 2017

IEEE Trans. Ind. Inf.

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Section: A Wavelet Coefficient (Wc)mentioning

confidence: 99%

Section: A Segment Processingmentioning

confidence: 99%

Section: B Nordic Grid Pmu Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Event Detection and Its Signal Characterization in PMU Data Stream

Negi¹,

Kishor

Uhlen

et al. 2017

IEEE Trans. Ind. Inf.

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“…The existing methods to classify disturbances are mostly based on time series analysis or conventional signal processing theory. For example, in [15] a Wavelet based method is presented for event detection by utilizing the PMU measurements. In [16], a combination of the Empirical Mode Decomposition and Spectral Kurtosis methods is introduced for event signal characterization based on PMU data stream.…”

Section: Introductionmentioning

confidence: 99%

Image Embedding of PMU Data for Deep Learning Towards Transient Disturbance Classification

Zhu¹,

Liu²,

Sun³

2018

2018 IEEE International Conference on Energy Internet (ICEI)

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This paper presents a study on power grid disturbance classification by Deep Learning (DL). A real synchrophasor set composing of three different types of disturbance events from the Frequency Monitoring Network (FNET) is used. An image embedding technique called Gramian Angular Field is applied to transform each time series of event data to a two-dimensional image for learning. Two main DL algorithms, i.e. CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) are tested and compared with two widely used data mining tools, the Support Vector Machine and Decision Tree. The test results demonstrate the superiority of the both DL algorithms over other methods in the application of power system transient disturbance classification.

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