6,283 research outputs found
Unbalanced load flow with hybrid wavelet transform and support vector machine based Error-Correcting Output Codes for power quality disturbances classification including wind energy
Purpose. The most common methods to designa multiclass classification consist to determine a set of binary classifiers and to combine them. In this paper support vector machine with Error-Correcting Output Codes (ECOC-SVM) classifier is proposed to classify and characterize the power qualitydisturbances such as harmonic distortion,voltage sag, and voltage swell include wind farms generator in power transmission systems. Firstly three phases unbalanced load flow analysis is executed to calculate difference electric network characteristics, levels of voltage, active and reactive power. After, discrete wavelet transform is combined with the probabilistic ECOC-SVM model to construct the classifier. Finally, the ECOC-SVM classifies and identifies the disturbance type according tothe energy deviation of the discrete wavelet transform. The proposedmethod gives satisfactory accuracy with 99.2% compared with well known methods and shows that each power quality disturbances has specific deviations from the pure sinusoidal waveform,this is good at recognizing and specifies the type of disturbance generated from the wind
power generator.ΠΠ°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½Π½ΡΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ ΠΌΡΠ»ΡΡΠΈΠΊΠ»Π°ΡΡΠΎΠ²ΠΎΠΉ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π·Π°ΠΊΠ»ΡΡΠ°ΡΡΡΡ Π² ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠΈ Π½Π°Π±ΠΎΡΠ° Π΄Π²ΠΎΠΈΡΠ½ΡΡ
ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΡΠΎΠ² ΠΈ ΠΈΡ
ΠΎΠ±ΡΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΠΈ. Π Π΄Π°Π½Π½ΠΎΠΉ ΡΡΠ°ΡΡΠ΅ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π° ΠΌΠ°ΡΠΈΠ½Π° ΠΎΠΏΠΎΡΠ½ΡΡ
Π²Π΅ΠΊΡΠΎΡΠΎΠ² Ρ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΡΠΎΠΌ Π²ΡΡ
ΠΎΠ΄Π½ΡΡ
ΠΊΠΎΠ΄ΠΎΠ² ΠΈΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΎΡΠΈΠ±ΠΎΠΊ(ECOC-SVM) Ρ ΡΠ΅Π»ΡΡ ΠΊΠ»Π°ΡΡΠΈΡΠΈΡΠΈΡΠΎΠ²Π°ΡΡ ΠΈ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΠΎΠ²Π°ΡΡ ΡΠ°ΠΊΠΈΠ΅ Π½Π°ΡΡΡΠ΅Π½ΠΈΡ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ, ΠΊΠ°ΠΊ Π³Π°ΡΠΌΠΎΠ½ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈΡΠΊΠ°ΠΆΠ΅Π½ΠΈΡ, ΠΏΠ°Π΄Π΅Π½ΠΈΠ΅ Π½Π°ΠΏΡΡΠΆΠ΅Π½ΠΈΡ ΠΈ ΡΠΊΠ°ΡΠΎΠΊ Π½Π°ΠΏΡΡΠΆΠ΅Π½ΠΈΡ, Π²ΠΊΠ»ΡΡΠ°Ρ Π³Π΅Π½Π΅ΡΠ°ΡΠΎΡ Π²Π΅ΡΡΠΎΠ²ΡΡ
ΡΠ»Π΅ΠΊΡΡΠΎΡΡΠ°Π½ΡΠΈΠΉ Π² ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
ΠΏΠ΅ΡΠ΅Π΄Π°ΡΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ. Π‘Π½Π°ΡΠ°Π»Π° Π²ΡΠΏΠΎΠ»Π½ΡΠ΅ΡΡΡ Π°Π½Π°Π»ΠΈΠ· ΠΏΠΎΡΠΎΠΊΠ° Π½Π΅ΡΠΈΠΌΠΌΠ΅ΡΡΠΈΡΠ½ΠΎΠΉ Π½Π°Π³ΡΡΠ·ΠΊΠΈ ΡΡΠ΅Ρ
ΡΠ°Π· Π΄Π»Ρ ΡΠ°ΡΡΠ΅ΡΠ° ΡΠ°Π·Π½ΠΎΡΡΠ½ΡΡ
Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ ΡΠ»Π΅ΠΊΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠ΅ΡΠΈ, ΡΡΠΎΠ²Π½Π΅ΠΉ Π½Π°ΠΏΡΡΠΆΠ΅Π½ΠΈΡ, Π°ΠΊΡΠΈΠ²Π½ΠΎΠΉ ΠΈ ΡΠ΅Π°ΠΊΡΠΈΠ²Π½ΠΎΠΉ ΠΌΠΎΡΠ½ΠΎΡΡΠΈ. ΠΠΎΡΠ»Π΅ ΡΡΠΎΠ³ΠΎ Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΠΎΠ΅ Π²Π΅ΠΉΠ²Π»Π΅Ρ-ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΎΠ±ΡΠ΅Π΄ΠΈΠ½ΡΠ΅ΡΡΡ Ρ Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠ½ΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΡΡ ECOC-SVM Π΄Π»Ρ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΡΠ°. ΠΠ°ΠΊΠΎΠ½Π΅Ρ, ECOC-SVM ΠΊΠ»Π°ΡΡΠΈΡΠΈΡΠΈΡΡΠ΅Ρ ΠΈ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΡΠΈΡΡΠ΅Ρ ΡΠΈΠΏ Π²ΠΎΠ·ΠΌΡΡΠ΅Π½ΠΈΡ Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ ΠΎΡΠΊΠ»ΠΎΠ½Π΅Π½ΠΈΠ΅ΠΌ ΡΠ½Π΅ΡΠ³ΠΈΠΈ Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΠΎΠ³ΠΎ Π²Π΅ΠΉΠ²Π»Π΅Ρ-ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ. ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΡΠΉ ΠΌΠ΅ΡΠΎΠ΄ Π΄Π°Π΅Ρ ΡΠ΄ΠΎΠ²Π»Π΅ΡΠ²ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΡΡ ΡΠΎΡΠ½ΠΎΡΡΡ 99,2% ΠΏΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ Ρ Ρ
ΠΎΡΠΎΡΠΎ ΠΈΠ·Π²Π΅ΡΡΠ½ΡΠΌΠΈ ΠΌΠ΅ΡΠΎΠ΄Π°ΠΌΠΈ ΠΈ ΠΏΠΎΠΊΠ°Π·ΡΠ²Π°Π΅Ρ, ΡΡΠΎ ΠΊΠ°ΠΆΠ΄ΠΎΠ΅ Π½Π°ΡΡΡΠ΅Π½ΠΈΠ΅ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ ΠΈΠΌΠ΅Π΅Ρ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΡΠ΅ ΠΎΡΠΊΠ»ΠΎΠ½Π΅Π½ΠΈΡ ΠΎΡ ΡΠΈΡΡΠΎ ΡΠΈΠ½ΡΡΠΎΠΈΠ΄Π°Π»ΡΠ½ΠΎΠΉ ΡΠΎΡΠΌΡ Π²ΠΎΠ»Π½Ρ, ΡΡΠΎ ΡΠΏΠΎΡΠΎΠ±ΡΡΠ²ΡΠ΅Ρ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ ΠΈ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΡΠΈΠΏΠ° Π²ΠΎΠ·ΠΌΡΡΠ΅Π½ΠΈΡ, Π³Π΅Π½Π΅ΡΠΈΡΡΠ΅ΠΌΠΎΠ³ΠΎ Π²Π΅ΡΡΠΎΠ²ΡΠΌ Π³Π΅Π½Π΅ΡΠ°ΡΠΎΡΠΎΠΌ
Geometric Wavelet Scattering Networks on Compact Riemannian Manifolds
The Euclidean scattering transform was introduced nearly a decade ago to
improve the mathematical understanding of convolutional neural networks.
Inspired by recent interest in geometric deep learning, which aims to
generalize convolutional neural networks to manifold and graph-structured
domains, we define a geometric scattering transform on manifolds. Similar to
the Euclidean scattering transform, the geometric scattering transform is based
on a cascade of wavelet filters and pointwise nonlinearities. It is invariant
to local isometries and stable to certain types of diffeomorphisms. Empirical
results demonstrate its utility on several geometric learning tasks. Our
results generalize the deformation stability and local translation invariance
of Euclidean scattering, and demonstrate the importance of linking the used
filter structures to the underlying geometry of the data.Comment: 35 pages; 3 figures; 2 tables; v3: Revisions based on reviewer
comment
Sparse Linear Models applied to Power Quality Disturbance Classification
Power quality (PQ) analysis describes the non-pure electric signals that are
usually present in electric power systems. The automatic recognition of PQ
disturbances can be seen as a pattern recognition problem, in which different
types of waveform distortion are differentiated based on their features.
Similar to other quasi-stationary signals, PQ disturbances can be decomposed
into time-frequency dependent components by using time-frequency or time-scale
transforms, also known as dictionaries. These dictionaries are used in the
feature extraction step in pattern recognition systems. Short-time Fourier,
Wavelets and Stockwell transforms are some of the most common dictionaries used
in the PQ community, aiming to achieve a better signal representation. To the
best of our knowledge, previous works about PQ disturbance classification have
been restricted to the use of one among several available dictionaries. Taking
advantage of the theory behind sparse linear models (SLM), we introduce a
sparse method for PQ representation, starting from overcomplete dictionaries.
In particular, we apply Group Lasso. We employ different types of
time-frequency (or time-scale) dictionaries to characterize the PQ
disturbances, and evaluate their performance under different pattern
recognition algorithms. We show that the SLM reduce the PQ classification
complexity promoting sparse basis selection, and improving the classification
accuracy
Graph Signal Processing: Overview, Challenges and Applications
Research in Graph Signal Processing (GSP) aims to develop tools for
processing data defined on irregular graph domains. In this paper we first
provide an overview of core ideas in GSP and their connection to conventional
digital signal processing. We then summarize recent developments in developing
basic GSP tools, including methods for sampling, filtering or graph learning.
Next, we review progress in several application areas using GSP, including
processing and analysis of sensor network data, biological data, and
applications to image processing and machine learning. We finish by providing a
brief historical perspective to highlight how concepts recently developed in
GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE
The wavelet transform for pre-processing IR spectra in the identification of mono- and di-substituted benzenes
This paper describes the wavelet transformation of IR spectra with the Daubechies analysing wavelet functions as a feature extracting method that successfully reduces the spectral data more than 20-fold with a significant improvement in the classification process
A Review of Fault Diagnosing Methods in Power Transmission Systems
Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field
Forecasting interest rates: A Comparative assessment of some second generation non-linear model
Modelling and forecasting of interest rates has traditionally proceeded in the framework of linear stationary models such as ARMA and VAR, but only with moderate success. We examine here four models which account for several specific features of real world asset prices such as non-stationarity and non-linearity. Our four candidate models are based respectively on wavelet analysis, mixed spectrum analysis, non-linear ARMA models with Fourier coefficients, and the Kalman filter. These models are applied to weekly data on interest rates in India, and their forecasting performance is evaluated vis-β¦-vis three GARCH models (GARCH (1,1), GARCH-M (1,1) and EGARCH (1,1)) as well as the random walk model. The Kalman filter model emerges at the top, with wavelet and mixed spectrum models also showing considerable promise.Interest rates, wavelets, mixed spectra, non-linear ARMA, Kalman filter, GARCH, Forecast encompassing
FORECASTING INTEREST RATES - A COMPARATIVE ASSESSMENT OF SOME SECOND GENERATION NON-LINEAR MODELS
Modelling and forecasting of interest rates has traditionally proceeded in the framework of linear stationary models such as ARMA and VAR, but only with moderate success. We examine here four models which account for several specific features of real world asset prices such as non-stationarity and non-linearity. Our four candidate models are based respectively on wavelet analysis, mixed spectrum analysis, non-linear ARMA models with Fourier coefficients, and the Kalman filter. These models are applied to weekly data on interest rates in India, and their forecasting performance is evaluated vis--vis three GARCH models (GARCH (1,1), GARCH-M (1,1) and EGARCH (1,1)) as well as the random walk model. The Kalman filter model emerges at the top, with wavelet and mixed spectrum models also showing considerable promise.interest rates, wavelets, mixed spectra, non-linear ARMA, Kalman filter, GARCH, Forecast encompassing.
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