Decoupling PI controller design for a normal conducting RF cavity using a recursive LEVENBERG-MARQUARDT algorithm

An effective and robust controller is designed using Levenberg-Marquardt (LM) algorithm-based Artificial Neural Network (ANN) for the solar Photo-Voltaic (PV) based distributed generation units for stabilizing the grid-connected microgrid (MG) under load changes and irradiance variations. A test system comprising of two PV units and one diesel generator unit connected to the utility grid is modelled and considered for the controller design in MATLAB/Simulink environment. PV generated power is injected into the grid through voltage source converter (VSC) regulated by using the proposed ANN controller. Based on the grid voltage and available PV generation, the ANN controller regulates the inverter current by setting the reference voltage vector to synthesize gating pulses for the inverter. The robustness of the controller design is analysed and validated through time-domain simulations by subjecting it to extreme operating conditions. The controller performance is evaluated by Integral Square Error (ISE) and Integral Time Absolute Error (ITAE) for the test system. The results are compared with conventional PI and PID controllers to prove the superior performing ANN controller.

Download Full-text

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.

Download Full-text