Interpolated DFT-Based Identification of Sub-Synchronous Oscillation Parameters Using Synchrophasor Data

Yang, Xiaomei; Zhang, Jianing; Xie, Xiaorong; Xiao, Xianyong; Gao, Bo; Wang, Yang

doi:10.1109/tsg.2019.2959811

Cited by 56 publications

(29 citation statements)

References 29 publications

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“…First, the Hanning window function is defined, with a length, specified by the SK frequency resolution. This window function is used because it has gradual increases in amplitude, touching zero at each end and, therefore provides an effective suppression of spectral leakage which can enhance FFT performance while having minimal disturbance of frequency resolution [ 32 , 33 ]. On non-periodic signals, as used in the Fourier transform, the Hanning window is able to improve amplitudes of interest when compared to the use of a rectangular window function [ 34 ].…”

Section: Methodsmentioning

confidence: 99%

Novel Prediction of Diagnosis Effectiveness for Adaptation of the Spectral Kurtosis Technology to Varying Operating Conditions

Kolbe

Gelman

Ball

2021

Sensors

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In this paper, two novel consistency vectors are proposed, which when combined with appropriate machine learning algorithms, can be used to adapt the Spectral Kurtosis technology for optimum gearbox damage diagnosis in varying operating conditions. Much of the existing research in the field is limited to test apparatus run in constant and carefully controlled operating conditions, and the authors have previously publicised that the Spectral Kurtosis technology requires adaptation to achieve the highest possible probabilities of correct diagnosis when a gearbox is run in non-stationary conditions of speed and load. However, the authors’ previous adaptation has been computationally heavy using a brute-force approach unsuited to online use, and therefore, created the requirement to develop these two newly proposed vectors and allow computationally lighter techniques more suited to online condition monitoring. The new vectors are demonstrated and experimentally validated on vibration data collected from a gearbox run in multiple combinations of operating conditions; for the first time, the two consistency vectors are used to predict diagnosis effectiveness, with the comparison and proof of relative gains between the traditional and novel techniques discussed. Consistency calculations are computationally light and thus, many combinations of Spectral Kurtosis technology parameters can be evaluated on a dataset in a very short time. This study shows that machine learning can predict the total probability of correct diagnosis from the consistency values and this can quickly provide pre-adaptation/prediction of optimum Spectral Kurtosis technology parameters for a dataset. The full adaptation and damage evaluation process, which is computationally heavier, can then be undertaken on a much lower number of combinations of Spectral Kurtosis resolution and threshold.

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Section: Methodsmentioning

confidence: 99%

Novel Prediction of Diagnosis Effectiveness for Adaptation of the Spectral Kurtosis Technology to Varying Operating Conditions

Kolbe

Gelman

Ball

2021

Sensors

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“…In power electronics enabled power systems, in addition to traditional low-frequency oscillations, SSOs induced by sub/supersynchronous interharmonics frequently occur [21]. Currently, PMUs are used for SSO monitoring [22], [23]. Therefore, the range of the oscillation signal identification extends from 45-55 Hz to 0-100 Hz.…”

Section: A Analysis Of Requirementsmentioning

confidence: 99%

“…The proposed method (threshold setting method A (SA)) is compared with the adaptive threshold setting method (threshold setting method B (SB)) proposed in [23]. The simulation signal model is described in (12) with a random noise 30 dB.…”

Section: Performance Verification 1) Simulation Testmentioning

confidence: 99%

Field PMU Test and Calibration Method—PartII: Test Signal Identification Methods and FieldTest Applications

Líu

2023

Journal of Modern Power Systems and Clean Energy

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Synchrophasor measurement units (PMUs) provide synchronized measurement data for wide-area applications. To improve the effectiveness of synchrophasor-based applications, field PMUs must be tested to ensure their performance and data quality. In the companion paper (Part I), we proposed a field PMU test and calibration framework consisting of a PMU calibrator and analysis center. Part I presents the development and test of the PMU calibrator. This paper focuses on the analysis center and field test applications. First, the critical component of the analysis center is the signal identification module, for which the step and oscillation signal identification methods are proposed. Here, the performance evaluation criteria of PMU in these two cases are different from others. The methods include a step signal detection method based on singular value decomposition (SVD), which has the capability of weak step detection to account for energy leakage of the signal during the step process, and an oscillation signal identification method based on SVD and fast Fourier transform, which can accurately extract oscillation components that benefit from the adaptive threshold setting method. Second, the analysis center software is implemented based on identification results. By integrating the PMU calibrator in Part I with the analysis center in Part II, we can examine in depth the field PMU test applications in three test scenarios, including standard, playback, and field signal test. Results demonstrate the effectiveness and applicability of the proposed field PMU test methods from both Parts I and II.

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“…The existing methods to achieve joint tracking of fundamental and subsynchronous components can be broadly categorised into (i) post‐processing of PMU data, (ii) block processing of measured data. In [7, 9, 10], there have been some efforts to obtain the SSO information from the fundamental phasor. However, the fundamental phasor‐based methods are not quite suitable for capturing the SSO dynamics, because the frequencies (including the SSO) generally have to be filtered out to avoid spectral leakage.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, these methods require a long observation window, which results in high latency of around 1 s [7]. Moreover, the method used in [10] can fail for higher SSO frequencies. On the other hand, the block processing based methods are mainly based on the discrete Fourier transform (DFT) analysis.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of subsynchronous oscillation in a series‐compensated wind power system using an adaptive extended Kalman filter

Shair

Xie

Yuan

et al. 2020

IET Renewable Power Generation

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The characteristics of subsynchronous oscillation (SSO) in series‐compensated wind power systems are significantly affected by the system's operating condition. Besides the variation in the magnitude and frequency of the SSO during an SSO event, the fundamental frequency may also deviate from its nominal value. This study aims at capturing the dynamics of both subsynchronous and fundamental frequency components simultaneously. This work first explores the strengths and weaknesses of various Kalman filtering based frequency tracking algorithms for joint tracking of the fundamental and SSO components. Then, it proposes a novel adaptive extended Kalman filtering (AEKF) algorithm, in which the process noise covariance is updated online by maximising the probability density function of the predicted error. The process noise covariance factor changes to positive non‐zero value whenever the frequency of the fundamental component deviates. Thus, the proposed AEKF extracts the time‐varying subsynchronous component while also tracking the small variations in the fundamental frequency. The tracking performance of the AEKF is validated on computer‐generated test signals as well as on the electromagnetic transient simulation model of an actual wind power system facing SSO. The captured fundamental and subsynchronous dynamics can be used for designing monitoring, protection, and control schemes for the SSO.

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Interpolated DFT-Based Identification of Sub-Synchronous Oscillation Parameters Using Synchrophasor Data

Cited by 56 publications

References 29 publications

Novel Prediction of Diagnosis Effectiveness for Adaptation of the Spectral Kurtosis Technology to Varying Operating Conditions

Novel Prediction of Diagnosis Effectiveness for Adaptation of the Spectral Kurtosis Technology to Varying Operating Conditions

Field PMU Test and Calibration Method—PartII: Test Signal Identification Methods and FieldTest Applications

Monitoring of subsynchronous oscillation in a series‐compensated wind power system using an adaptive extended Kalman filter

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