A Novel Method for Determining Harmonic Emission Responsibilities at the Point of Common Coupling

2013 ◽  
Vol 448-453 ◽  
pp. 2380-2383
Author(s):  
Xiao Jun Zhu ◽  
Xiang Li ◽  
Bin Fu ◽  
Ang Fu ◽  
Min You Chen ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares Regression ◽  
Least Squares Regression ◽  
Harmonic Emission ◽  
Point Of Common Coupling ◽  
Practical Engineering ◽  
Data Points ◽  
Harmonic Voltage ◽  
Novel Method ◽  
Common Coupling

This paper presents a novel method for determining the harmonic emission responsibilities of utility and customer at the point of common coupling (PCC). The proposed approach is based on robust partial least squares regression (robust PLS), which estimates system harmonic impedance by utilizing the signals of harmonic voltage and current measured synchronously at PCC. Consequently the harmonic emission responsibilities are calculated. The presented method reduces or removes the effect of outlying data points. The simulation and the practical engineering results indicate that the proposed method is valid and feasible.

Assessing the Harmonic Emission Level Based on Partial Least-Squares Regression with Data Envelopment Analysis

2014 ◽  
Vol 989-994 ◽  
pp. 3367-3370
Author(s):  
Xiang Li ◽  
Min You Chen ◽  
Yong Wei Zheng
Keyword(s):  
Data Envelopment Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Accurate Estimation ◽  
Data Envelopment ◽  
Least Squares Regression ◽  
Emission Level ◽  
Harmonic Emission ◽  
Point Of Common Coupling ◽  
Harmonic Emission Level

A novel method is used for assessing the harmonic emission level, which is based on the partial least-squares (PLS) regression with data envelopment analysis (DEA). Based on measuring the harmonic voltage and current at the point of common coupling (PCC) and removing the inefficiency data with DEA, regression coefficients are worked out through partial least-squares algorithm. Consequently the harmonic emission level of customer is calculated.The proposed approach removes the effect of outlying data points and gets accurate estimation results. The simulation results prove that the proposed method is more effective than PLS.

scholarly journals Method for Estimating Harmonic Parameters Based on Measurement Data without Phase Angle

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 879 ◽  
Author(s):  
Fulin Zhou ◽  
Feifan Liu ◽  
Ruixuan Yang ◽  
Huanrui Liu
Keyword(s):  
Power Quality ◽  
Phase Difference ◽  
Measurement Data ◽  
Power Grids ◽  
Least Squares Regression ◽  
Phase Information ◽  
Point Of Common Coupling ◽  
Practical Engineering ◽  
General Power ◽  
Harmonic Voltage

The excessive use of power electronics makes power quality problems in power grids increasingly prominent. The estimation of the harmonic parameters of harmonic sources in the power grid and the division of harmonic responsibilities are of great significance for the evaluation of power quality. At present, methods for estimating harmonic parameters and harmonic responsibilities need to provide the amplitude and phase information of the current and voltage of the point of common coupling (PCC). However, in practical engineering applications, the general power quality monitor only provides the amplitude information of the voltage and current of the measured point and the phase difference information between them. Missing phase information invalidates existing methods. Based on the partial least squares regression method, the present work proposes a method for estimating harmonic parameters in the case of monitoring data without phase. This method only needs to measure the amplitude information of the harmonic voltage and current of the PCC and the phase difference between them, then use the measurable data to estimate the harmonic parameters and the harmonic responsibility of each harmonic source. It provides a new way to effectively solve the problem that the measured data of the project has no phase information. The feasibility and effectiveness of the proposed method are proved by simulation data and measured engineering data.

A rapid and novel method for predicting nicotine alkaloids in tobacco through electronic nose and partial least-squares regression analysis

2016 ◽  
Vol 8 (7) ◽  
pp. 1609-1617 ◽  
Author(s):  
Shunshun Lin ◽  
Xiaoming Zhang
Keyword(s):  
Regression Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Electronic Nose ◽  
Partial Least Squares Regression ◽  
Nicotine Addiction ◽  
Least Squares Regression ◽  
Associated Diseases ◽  
Novel Method

Alkaloid levels in tobacco are of great concern owing to nicotine addiction and associated diseases.

Use of reflectance spectroscopy to estimate the organic carbon and CaCO3 contents of soils

2012 ◽  
Vol 61 (2) ◽  
pp. 277-290 ◽  
Author(s):  
Ádám Csorba ◽  
Vince Láng ◽  
László Fenyvesi ◽  
Erika Michéli
Keyword(s):  
Organic Carbon ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Mean Squared Error ◽  
Reflectance Spectroscopy ◽  
Least Squares Regression ◽  
Root Mean Squared Error ◽  
Squared Error

Napjainkban egyre nagyobb igény mutatkozik olyan technológiák és módszerek kidolgozására és alkalmazására, melyek lehetővé teszik a gyors, költséghatékony és környezetbarát talajadat-felvételezést és kiértékelést. Ezeknek az igényeknek felel meg a reflektancia spektroszkópia, mely az elektromágneses spektrum látható (VIS) és közeli infravörös (NIR) tartományában (350–2500 nm) végzett reflektancia-mérésekre épül. Figyelembe véve, hogy a talajokról felvett reflektancia spektrum információban nagyon gazdag, és a vizsgált tartományban számos talajalkotó rendelkezik karakterisztikus spektrális „ujjlenyomattal”, egyetlen görbéből lehetővé válik nagyszámú, kulcsfontosságú talajparaméter egyidejű meghatározása. Dolgozatunkban, a reflektancia spektroszkópia alapjaira helyezett, a talajok ösz-szetételének meghatározását célzó módszertani fejlesztés első lépéseit mutatjuk be. Munkánk során talajok szervesszén- és CaCO3-tartalmának megbecslését lehetővé tévő többváltozós matematikai-statisztikai módszerekre (részleges legkisebb négyzetek módszere, partial least squares regression – PLSR) épülő prediktív modellek létrehozását és tesztelését végeztük el. A létrehozott modellek tesztelése során megállapítottuk, hogy az eljárás mindkét talajparaméter esetében magas R2értéket [R2(szerves szén) = 0,815; R2(CaCO3) = 0,907] adott. A becslés pontosságát jelző közepes négyzetes eltérés (root mean squared error – RMSE) érték mindkét paraméter esetében közepesnek mondható [RMSE (szerves szén) = 0,467; RMSE (CaCO3) = 3,508], mely a reflektancia mérési előírások standardizálásával jelentősen javítható. Vizsgálataink alapján arra a következtetésre jutottunk, hogy a reflektancia spektroszkópia és a többváltozós kemometriai eljárások együttes alkalmazásával, gyors és költséghatékony adatfelvételezési és -értékelési módszerhez juthatunk.

Speech Emotion Recognition Based on Sparse Representation

2013 ◽  
Vol 38 (4) ◽  
pp. 465-470 ◽  
Author(s):  
Jingjie Yan ◽  
Xiaolan Wang ◽  
Weiyi Gu ◽  
LiLi Ma
Keyword(s):  
Dimensionality Reduction ◽  
Emotion Recognition ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Speech Emotion Recognition ◽  
Least Squares Regression ◽  
Computer Science Pedagogy ◽  
Reduction Methods ◽  
Analysis Computer

Abstract Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.

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