PMU-Based Vulnerability Assessment Using Wide- Area Severity Indices and Tracking Modal Analysis

Kamwa, Innocent; Beland, J.; McNabb, D.

doi:10.1109/psce.2006.296288

Cited by 29 publications

(14 citation statements)

References 10 publications

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“…In fact, denoting by the th component of the AM-FM signal in (1) and assuming a constant initial phase , it is proven that [12], [13] (4) with a negligible approximation error under quite general and realistic conditions. This motivated the energy separation algorithm (ESA) for tracking the instantaneous amplitude and frequency as follows [13], [14]: (5) Note that, in the case of a monochromatic signal with constant and , the relationships (5) are exact. When the continuous energy operator (3) is sampled with a sampling period T, the following equivalent discrete energy operator is obtained [13]: (6) It results in (7) …”

Section: Overview Of the Teager-kaiser's Energy Operatormentioning

confidence: 99%

“…O SCILLATION monitoring [1]- [5] is a topic of increasing recent attention, owing to a heightened interest in the prevention of widespread blackouts, which can result from uncontrolled power swings across large geographical areas. Basically, the entity responsible for this task should first determine from power system response signals when/if there is an oscillation issue and then quantify the underlying threat by means of an accurate assessment of the frequency and damping of the oscillation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust Detection and Analysis of Power System Oscillations Using the Teager-Kaiser Energy Operator

Kamwa

Pradhan

Joós

2011

IEEE Trans. Power Syst.

109

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Critical to real-time oscillations monitoring is early detection when otherwise dormant natural modes become a serious threat to grid stability. The next urgent issue is to determine the frequency and damping of the problematic modes when the signal is embedded in noise and the system contains closely spaced natural modes. The present paper addresses the detection issue using the Teager-Kaiser energy operator (TKEO) which has shown to be a fast predictor of the instability onset time when applied to the output signals of an orthogonal filter bank. In the system stability context, linear filter decomposition (LFD) is preferred rather than empirical mode decomposition (EMD), well known for its tendency to generate artificial modes with no physical meaning. A narrowband LFD with a less than 0. 2-Hz bandwidth is achieved in the range 0.05 to 3 Hz through a cosine-modulated filter bank design. The effectiveness of the scheme in accurately detecting and tracking the frequency and damping of oscillatory modes is demonstrated using Monte Carlo simulations of three closely spaced modes and a detailed analysis of an actual event recorded by Hydro-Québec's WAMS in 2006.Index Terms-Filter bank, interarea oscillation, power system identification, power system monitoring, power system oscillations, power system stability, Teager-Kaiser energy (TKE), wide-area measurement systems (WAMS).

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Section: Overview Of the Teager-kaiser's Energy Operatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Detection and Analysis of Power System Oscillations Using the Teager-Kaiser Energy Operator

Kamwa

Pradhan

Joós

2011

IEEE Trans. Power Syst.

109

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“…A logic-based method to identify a contingency severity and to assess voltage stability from PMU data is proposed in [15]. A vulnerability assessment method for quickly assessing the post-contingency status of a multi-area power system, using PMU data and Wide Area Severity Indices (WASI), is developed in [16]. These WASI are calculated in the frequency domain through applying the Short-Time-Fourier-Transform (STFT).…”

Section: Pmu-based Vulnerability Assessmentmentioning

confidence: 99%

Dynamic vulnerability assessment due to transient instability based on data mining analysis for Smart Grid applications

Cepeda

Colomé

Castrillón³

2011

2011 Ieee Pes Conference on Innovative Smart Grid Technologies Latin America (Isgt La)

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In recent years, some Smart Grid applications have been designed in order to perform timely Self-Healing and adaptive reconfiguration actions based on system-wide analysis, with the objective of reducing the risk of power system blackouts. Real time dynamic vulnerability assessment (DVA) has to be done in order to decide and coordinate the appropriate corrective control actions, depending on the event evolution. This paper presents a novel approach for carrying out real time DVA, focused on Transient Stability Assessment (TSA), based on some time series data mining techniques (Multichannel Singular Spectrum Analysis MSSA, and Principal Component Analysis PCA), and a machine learning tool (Support Vector Machine Classifier SVM-C). In addition, a general overview of the state of the art of the methods to perform vulnerability assessment, with emphasis in the potential use of PMUs for post-contingency DVA, is described. The developed methodology is tested in the IEEE 39 bus New England test system, where the simulated cause of vulnerability is transient instability. The results show that time series data mining tools are useful to find hidden patterns in electric signals, and SVM-C can use those patterns for effectively classifying the system vulnerability status.

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“…In this aim, a post-contingency VA method for quickly assessing the multi-area vulnerability status is developed in [4]. This method uses PMU data to compute Wide Area Severity Indices (WASI).…”

Section: Introductionmentioning

confidence: 99%

Benefits of empirical orthogonal functions in pattern recognition applied to vulnerability assessment

Cepeda¹,

Colomé

2014

2014 IEEE PES Transmission &Amp; Distribution Conference and Exposition - Latin America (PES T&D-La)

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One of the main challenges of power system vulnerability assessment is to handle adequate tools capable of analyzing huge volumes of data in real time. For instance, phasor measurement units (PMUs) offer lots of dynamic time series data regarding phasor electric signals. This dynamic data can be analyzed via signal processing tools, such as Fourier Transform, in order to obtain patterns that alert about possible system stress. However, due to the specific dynamic behavior of postcontingency electric signals, they do not exactly correspond to pure periodic signals. Thus, applying classical Fourier-related transforms might not allow obtaining relevant patterns from these types of signals. In this connection, the present paper proposes to use a well-proven time series data mining technique, called empirical orthogonal functions (EOFs), instead of Fourierrelated transforms. EOFs better adapt to the peculiar shape of the post-contingency system variables, which allows getting a better pattern recognition, and so a better vulnerability assessment. With the aim of showing the benefits of EOF, a fair comparison of this data mining tool with Discrete Fourier Transform (DFT) is presented in this paper via three different signal examples. IndexTerms-Electric power systems, vulnerability assessment, data mining, empirical orthogonal functions (EOF), Discrete Fourier Transform (DFT).

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PMU-Based Vulnerability Assessment Using Wide- Area Severity Indices and Tracking Modal Analysis

Cited by 29 publications

References 10 publications

Robust Detection and Analysis of Power System Oscillations Using the Teager-Kaiser Energy Operator

Robust Detection and Analysis of Power System Oscillations Using the Teager-Kaiser Energy Operator

Dynamic vulnerability assessment due to transient instability based on data mining analysis for Smart Grid applications

Benefits of empirical orthogonal functions in pattern recognition applied to vulnerability assessment

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