An Evaluation Method for Harmonic Emission Level Based on Principal Component Regression

Abstract

To identify the responsible party of harmonic pollution, this paper puts forward a novel estimation method for harmonic emission level based on principal component regression (PCR). After introducing the principles of the PCR, the author set up a regression equation based on the complex relationship between harmonic voltage, harmonic current and harmonic impedance of the supply system at the point of common coupling (PCC). Then, the PCR was introduced to estimate the harmonic emission levels of the supply system and the consumer at the PCC. The validity of our method was verified through both simulation and field test. The results show that our method can accurately estimate the harmonic emission level, while overcoming the multicollinearity of independent variables and retaining important information in regression.

A Correlation Analysis Model of Human Factors in Mine Accidents Based on Apriori Algorithm

“…Then, the variables whose cumulative contribution is above 85% were treated as the new input sample of RBFNN simulation. Prior to the PCA, the raw data were normalized, including but not limited to quantification of qualitative indies, forward processing of reverse indices, removal of extremums [23].…”

“…Then, the variables whose cumulative contribution is above 85% were treated as the new input sample of RBFNN simulation. Prior to the PCA, the raw data were normalized, including but not limited to quantification of qualitative indies, forward processing of reverse indices, removal of extremums[23].As for the RBFNN parameters, the center and variance of hidden layer basis function were learned through selforganization, while the weight between the hidden layer and the output layer was determined through supervised learning.…”