A Linear Regression Model for THD Sensitivity in PV Based Microgrid with Varying Insolation Levels

Solar PV systems can be used for powering small microgrids in rural area of developing countries. Generally, a solar power microgrid consists of a PV array, an MPPT, a dc-dc converter and an inverter, particularly as the general loads are A.C in nature. In a PV system, reactive current, unbalancing in currents, and harmonics are generated due to the power electronics-based converters as well as nonlinear loads (computers induction motors etc). Thus, estimation of the harmonics levels measured by the Total Harmonic Distortion (THD) is an essential aspect of performance assessment of a solar powered microgrid. A major issue that needs to be examined is the impact of PV system control parameters on the THD. In this paper, we take up this assessment for a small PV based rural microgrid with varying levels of solar irradiance. A Simulink model has been developed for the study from which the THD at equilibrium conditions is estimated. This data is in turn used to design a generalized Linear Regression Model, which can be used to observe the sensitivity of three control variables on the magnitude of the THD. These variables are: Solar Irradiance levels, Power Factor (PF) of connected load magnitude of the connected load (in kVA) The results obtained show that the greatest sensitivity is obtained for load kVA variation.

An alternative look at the linear regression model

An alternative look at the linear regression model is taken by proposing an original treatment of a full column rank model (design) matrix. In such a situation, the Moore–Penrose inverse of the matrix can be obtained by utilizing a particular formula which is applicable solely when a matrix to be inverted can be columnwise partitioned into two matrices of disjoint ranges. It turns out that this approach, besides simplifying derivations, provides a novel insight into some of the notions involved in the model and reduces computational costs needed to obtain sought estimators. The paper contains also a numerical example based on astronomical observations of the localization of Polaris, demonstrating usefulness of the proposed approach.

Influence of the key account manager in the provisioning management: Evidence from staple companies during the events of COVID-19

The purpose of this research was to evaluate the degree of influence exercised by the Key Account Manager in the provisioning management in the main companies called Staple in Peru, during the events of COVID-19. The research was of type quantitative, cross-sectional and temporal, with a non-experimental design, using a multiple linear regression model and correlation analysis to determine the impact that exists between the variables. The data belongs to the Industrias San Miguel company, distributed in a weekly period from June 2019 to March 2021, which gives 88 observations. The results allow us to conclude that the Key Account Manager is an important manager of the supply of goods during the crisis caused by COVID-19 in staple companies.

Study On Spectral Characters-Chlorophyll Inversion Model of Sabina Vulgaris in Mu Us Sandy Land

Sabina vulgaris is a group tree species in Mu Us Sandy Land. Understanding the growth status of Sabina vulgaris has guiding value for vegetation change monitoring. Chlorophyll is an important indicator to characterize the growth status of plants, and its content changes are important for analyzing the physiological growth status of plants and guiding the precise planting of plants. In this paper, the spectral reflectance and chlorophyll content of Sabina vulgaris were measured by SVC HR-1024 portable ground feature spectrometer and SPAD502 chlorophyll instrument, and the relationship between ground feature spectral characteristics and chlorophyll content of Sabina vulgaris was studied. The results show that there is a correlation between the vegetation index and chlorophyll, the effect of NDVI is the best, the bands with the highest correlation are the combined bands of 470nm-500nm, 610nm-680nm, and 740nm-840nm, and the wavelengths with the highest correlation are (660,790); Vegetation index, red-edge parameters, and chlorophyll have a certain correlation. The fitting effect of the model established by vegetation index is better than that established by red-edge parameters, and the highest R2 is 0.97; Among the three modeling methods, the model fitting effect of partial least squares is the best, R2 is > 0.91, and the disadvantage is that the processing process is complex; The processing method of the univariate linear regression model is the simplest, but the disadvantage is that the accuracy of the model is unstable, R2 is between 0.1-0.9, so the multivariate linear regression model is the most suitable of the three methods(R2>0.8).