Controlling Power System of Earth Station Using Embedded System
01 Jan 2018-pp 95-102
TL;DR: Using neural network, this work developed algorithm in embedded C as per different types of failure, battery voltage, and solar voltage as well as main source of earth station and diesel generator for failure conditions.
Abstract: We have used two methods for earth station power controller is designed by Cortex M3 by using LPC 1768 board and FPGA board. Different loads having different power requirements and different fault conditions occurred. Earth station having 800 W inverter and having two failures named hard failure and soft failure. We have used solar energy as a main source of earth station and diesel generator for failure conditions. Using neural network, we developed algorithm in embedded C as per different types of failure, battery voltage, and solar voltage.
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TL;DR: In this paper, a neural network based maximum power tracking controller for interconnected PV power systems is presented, where the neural network is utilized to identify the optimal operating voltage of the PV power system.
Abstract: This paper presents a neural network based maximum power tracking controller for interconnected PV power systems The neural network is utilized to identify the optimal operating voltage of the PV power system The controller generates the control signal in real-time, and the control signal is fed back to the voltage control loop of the inverter to shift the terminal voltage of the PV power system to its identified optimum, which yields maximum power generation The controller is of the PI type The proportional and the integral gains are set to their optimal values to achieve fast response and also to prevent overshoot and also undershoot Continuous measurement is required for the open circuit voltage on the monitoring cell, and also for the terminal voltage of the PV power system Because of the accurate identification of the optimal operating voltage of the PV power system, more than 99% power is drawn from the actual maximum power >
169 citations
28 Nov 2005
TL;DR: In this article, the authors presented the development of a maximum power point tracking algorithm using an artificial neural network for a solar power system by applying a three layers neural network and some simple activation functions.
Abstract: This paper presents the development of a maximum power point tracking algorithm using an artificial neural network for a solar power system By applying a three layers neural network and some simple activation functions, the maximum power point of a solar array can be efficiently tracked The tracking algorithm integrated with a solar-powered battery charging system has been successfully implemented on a low-cost PIC16F876 RISC-microcontroller without external sensor unit requirement The experimental results with a commercial solar array show that the proposed algorithm outperforms the conventional controller in terms of tracking speed and mitigation of fluctuation output power in steady state operation The overall system efficiency is well above 91%
17 citations
TL;DR: The results shows that a good agreement between MATLAB and VHDL and a fast and flexible feed forward NN which is capable of dealing with floating point arithmetic operations; minimum number of CLB slices; and good speed of performance is shown.
Abstract: Automation of generating hardware description language code from neural networks models can highly decrease time of implementation those networks into a digital devices, thus significant money savings. To implement the neural network into hardware designer, it is required to translate generated model into device structure. VHDL language is used to describe those networks into hardware. VHDL code has been proposed to implement ANNs as well as to present simulation results with floating point arithmetic of the earth station and the satellite power systems using ModelSim ® PE 6.6 simulator tool. Integration between MATLAB ® and VHDL is used to save execution time of computation. The results shows that a good agreement between MATLAB and VHDL and a fast/flexible feed forward NN which is capable of dealing with floating point arithmetic operations; minimum number of CLB slices; and good speed of performance. FPGA synthesis results are obtained with view RTL schematic and technology schematic from Xilinix tool. Minimum number of utilized resources is obtained by using Xilinix VERTIX5.
15 citations