Identification of Instantaneous Attributes of Torsional Shaft Signals Using the Hilbert Transform

Andrade, Manuel A.; Messina, A.R.; Rivera, C.A.; Olguı́n, D.

doi:10.1109/tpwrs.2004.829664

Cited by 59 publications

(11 citation statements)

References 11 publications

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“…The main advantage of the proposed algorithm, in contrast to the algorithm proposed in [24], is that it does not need to use Hilbert-Huang techniques since these techniques require global information for the analysis [22], [23], [26]. Meanwhile, the proposed methodology only uses local temporary information, and it is suitable for online applications.…”

Section: Discussionmentioning

confidence: 99%

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Single Value Decomposition to Estimate Critical Clearing Time of a Power System Using Measurements

et al. 2021

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The transient stability analysis of large-interconnected power systems using time-domain simulations (TDS) is a significant challenge since it represents a huge computational cost. Besides, for dynamic security assessment is required have a quick response. Consequently, recent approaches are relying on using the wide-area measurement system combined with other techniques to perform transient stability assessment and counteract the drawbacks of the TDS method. However, these approaches still requiring to perform TDS to set initial parameters. This paper proposes a new algorithm to estimate the critical clearing time (CCT) based on the eigenvalue calculation and the singular value decomposition using data from wide-area measurement systems. The proposed algorithm uses the phase angles of the voltage phasors measurements at the generation buses to represent the dynamics of the internal angles of the generators. First, from a set of signals, a measurement matrix is formed using a sliding window. Then, a threshold based on the maximum singular value and the dominant eigenvalue of the measurement matrix are computed. Finally, the CCT is estimated using the dominant eigenvalue (the most energetic eigenvalue) and the threshold. The proposed algorithm is evaluated using the Kundur four-machine system and New England 39-Bus system. Its performance contrasts to the CCT calculated using the classical TDS. The simulation results demonstrated acceptable precision of the CCT against TDS. Also, it presents robustness against the effect of the noise in the measurements. Therefore, it is suitable for online applications.

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Section: Discussionmentioning

confidence: 99%

“…Finally, PCA is applied to detect and extract unusual dynamic events from measured data [25]. The POD and Hilbert analysis approach is also used to determine the characteristics of the torsional shaft signals [26]. Moreover, the SVD method has been used to identify and form coherent groups [27], [28].…”

Section: Introductionmentioning

confidence: 99%

Single Value Decomposition to Estimate Critical Clearing Time of a Power System Using Measurements

et al. 2021

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“…A signal can be analyzed in details for its frequency, amplitude and phase contents by using EMD followed by HT [40]. The method has been applied to many important problems in various fields including medical [41], geophysics [42] and power engineering [43]. Jayasree et al [44] employed automated classification of power quality disturbances using HHT and RBF neural networks.…”

Section: Iv4 Hilbert Huang Transformmentioning

confidence: 99%

Classification of Power Quality Disturbances in Electric Power System: A Review

Mittal¹

2012

IOSRJEEE

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The Electrical power quality issue has attained considerable attention in last decade due to large penetration of power electronics based loads and microprocessor based controlled loads. These devices introduce power quality problem on one hand and these mal-operate due to induced power quality problems on other hand. The various techniques for classification or recognition of power quality disturbances in electrical power system have been proposed so far in different papers. This paper presents a comprehensive overview of different techniques used for feature extraction and classification of power disturbance. This paper helps the researchers to know about the different methods presented so far for power quality disturbance classification or recognition, so that further work on power quality improvement can be carried out for better results.

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“…By contrast, Hilbert-Huang transform (HHT) is not subject to other uncertainty limitations for time and frequency resolutions. HHT is a completely self-adapting approach for the identification of damage time instant and location in civil and mechanical structures (Andrade et al 2004, Liu et al 2006, Messina & Vittal 2005, Sanchez-Gasca et al 2005, Ruiz-Vega et al 2005.…”

Section: Introductionmentioning

confidence: 99%

Time-frequency analysis of DC bias vibration of transformer core on the basis of Hilbert–Huang transform

Liu

Yang

et al. 2016

ing.inv.

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This paper presents a time-frequency analysis of the vibration of transformer under direct current (DC) bias through Hilbert-Huang transform (HHT). First, the theory of DC bias for the transformer was analyzed. Next, the empirical mode decomposition (EMD) process, which is the key in HHT, was introduced. The results of EMD, namely, intrinsic mode functions (IMFs), were calculated and summed by Hilbert transform(HT) to obtain time-dependent series in a 2D time-frequency domain. Lastly, a test system of vibration measurement for the transformer was set up. Three direction (x, y, and z axes) components of core vibration were measured. Decomposition of EMD and HHT spectra showed that vibration strength increased, and odd harmonics were produced with DC bias. Results indicated that HHT is a viable signal processing tool for transformer health monitoring.Keywords: DC bias, transformer, vibration, HHT. RESUMENEn este trabajo se presenta un análisis tiempo-frecuencia de la vibración del transformador bajo polarización de corriente continua (CC) a través de transformación Hilbert-Huang (HHT). En primer lugar, se analizó la teoría de la DC de polarización para el transformador. A continuación, el proceso de descomposición de modo empírico (EMD), que es la clave en HHT, se introdujo. Los resultados de EMD, a saber, las funciones del modo intrínsecas (IMFS), se calcularon y se suman por transformada de Hilbert (HT) para obtener series dependienteS de tiempo en un dominio de tiempo-frecuencia 2D. Por último, un sistema de prueba de medición de vibraciones para el transformador se creó. Se midieron tres componentes de dirección de vibración del núcleo (ejes x, y, y z). La descomposición de los espectros EMD y HHT mostró que la fuerza de la vibración aumentó, y los armónicos impares se produjeron con polarización CC. Los resultados indicaron que la HHT es una herramienta de procesamiento de señales viable para la vigilancia de la salud del transformador.

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Identification of Instantaneous Attributes of Torsional Shaft Signals Using the Hilbert Transform

Cited by 59 publications

References 11 publications

Single Value Decomposition to Estimate Critical Clearing Time of a Power System Using Measurements

Single Value Decomposition to Estimate Critical Clearing Time of a Power System Using Measurements

Classification of Power Quality Disturbances in Electric Power System: A Review

Time-frequency analysis of DC bias vibration of transformer core on the basis of Hilbert–Huang transform

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