Systematic Framework for Reflectarray Synthesis Based on Phase Optimization

A new systematic synthesis framework for reflectarray antennas is discussed. Optimization based on the Levenberg-Marquardt algorithm is used to obtain the phase distribution of the reflection coefficients required on the reflectarray surface, in order to achieve the pattern specifications. A Local Multipoint Distribution System (LMDS) base station working in the 24.5–26.5 GHz frequency band has been proposed to evaluate the method. The 3D requirements are defined by the combination of the elevation and templates and considering a maximum acceptable ripple in the beam shaping. Some illustrative results are obtained.

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Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups

Revista de Chimie ◽

10.37358/rc.20.6.8171 ◽

2020 ◽

Vol 71 (6) ◽

pp. 66-74

Author(s):

Younis M. Younis ◽

Salman H. Abbas ◽

Farqad T. Najim ◽

Firas Hashim Kamar ◽

Gheorghe Nechifor

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Diesel Fuel ◽

Back Propagation ◽

Second Step ◽

Heating Value ◽

Network Training ◽

Marquardt Algorithm ◽

Levenberg Marquardt ◽

Propagation Network

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

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Unit Template Based Control of PV-DSTATCOM

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096511666181018112853 ◽

2020 ◽

Vol 13 (1) ◽

pp. 36-42

Author(s):

Gunjan Varshney ◽

Durg S. Chauhan ◽

Madhukar P. Dave ◽

Nitin

Keyword(s):

Power Quality ◽

Power Factor ◽

Power Distribution ◽

Distribution System ◽

Distribution Systems ◽

Phase Distribution ◽

Voltage Regulation ◽

Photovoltaic Array ◽

Quality Issues ◽

Static Compensator

Background: In modern electrical power distribution systems, Power Quality has become an important concern due to the escalating use of automatic, microprocessor and microcontroller based end user applications. Methods: In this paper, power quality improvement has done using Photovoltaic based Distribution Static Compensator (PV-DSTATCOM). Complete simulation modelling and control of Photovoltaic based Distribution Static Compensator have been provided in the presented paper. In this configuration, DSTATCOM is fed by solar photovoltaic array and PV module is also helpful to maintain the DC link voltage. The switching of PV-STATCOM is controlled by Unit template based control theory. Results: The performance of PV-DSTATCOM has been evaluated for Unity Power Factor (UPF) and AC Voltage Control (ACVC) modes. Here, for studying the power quality issues three-phase distribution system is considered and results have been verified through simulation based on MATLAB software. Conclusion: Different power quality issues and their improvement are studied and presented here for harmonic reduction, DC voltage regulation and power factor correction.

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