Noppadol Khaehintung

FPGA Implementation of MPPT Using Variable Step-Size P&O Algorithm for PV Applications

This paper describes FPGA implementation of a maximum power point tracking (MPPT) for photovoltaic (PV) applications. By slightly modifying the original algorithm, an improved variable step-size P&O algorithm is realized and efficiently implemented using a hardware description language (VHDL). Subsequently, the new MPPT algorithm integrated with a solar-powered battery charging system is implemented on the XC2C384 FPGA without external sensor unit requirement. Experimental results with a commercial PV 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 96%

RISC-microcontroller built-in fuzzy logic controller of maximum power point tracking for solar-powered light-flasher applications

This paper presents the development of maximum power point tracking (MPPT) using a fuzzy logic controller (FLC). By applying the synthetic fuzzy inference algorithm, the relationship between input and output of FLC can be effectively stored in a memory-limited lookup table (LUT). As a consequence, the controller can be efficiently implemented on a low-cost 16F872 RISC microcontroller. A practical system found in a transportation industry, particularly a solar-powered light-flasher (SPLF) with built in MPPT using FLC, is developed. Simulations with practical parameters show that our proposed MPPT using FLC implemented by LUT outperforms the conventional MPPT controller in terms of tracking speed. Furthermore, experimental results are shown to demonstrate the superiority of the proposed technique.

A novel ANFIS controller for maximum power point tracking in photovoltaic systems

This paper presents the design of a controller for maximum power point tracking (MPPT) of a photovoltaic system. The proposed controller relies upon an adaptive neuro-fuzzy inference system (ANFIS) which is designed as a combination of the concepts of Sugeno fuzzy model and neural network. The controller employs the ANFIS of five layers with nine fuzzy rules. Simulations with practical parameters show that our proposed MPPT using ANFIS outperform the conventional MPPT controller terms of tracking speed and accuracy. Moreover, the system is implemented on Pentium PC, from which preliminary experimental results are presented.

A Solar-powered Battery Charger with Neural Network Maximum Power Point Tracking Implemented on a Low-Cost PIC-microcontroller

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%

RISC microcontroller built-in fuzzy logic controller for maximum power point tracking in solar-powered for battery charger

This paper presents the development of maximum power point tracking (MPPT) using a fuzzy logic controller (FLC). By applying the synthetic fuzzy inference algorithm, the relationship between input and output of FLC can be effectively stored in a memory-limited lookup table (LUT). As a consequence, the controller can be efficiently implemented on a low-cost 16F872 RISC microcontroller. The proposed controller is integrated with a boost converter for realization of a high performance solar-powered battery charger (SPBC). Simulations with practical parameters show that our proposed MPPT using FLC implemented by LUT outperforms the conventional MPPT controller in terms of tracking speed. Moreover, preliminary experimental results are presented.

Application of Maximum Power Point Tracker with Self-organizing Fuzzy Logic Controller for Solar-powered Traffic Lights

This paper presents the development of maximum power point tracking (MPPT) using an adjustable self-organizing fuzzy logic controller (SOFLC) for a solar-powered traffic light equipment (SPTLE) with an integrated maximum power point tracking (MPPT) system on a low-cost microcontroller. The proposed system is integrated with a boost converter for realizing of high performance SPTLE, whose adaptability properties are very attractive for operation of a solar array power tracking in dynamic environments. The proposed MPPT scheme obtained by varying the duty ratio for DC- DC boost converter has been successfully implemented on a low-cost PIC16F876A RISC-microcontroller. Experimental results of the hardware prototypes for SPTLE, light flasher and light chevron, with commercial solar array show that our proposed MPPT using SOFLC as compared with fuzzy logic controller (FLC) in terms of tracking speed with 92% of overall system efficiency.

Implementation of GA-trained GRNN for Intelligent Fast Charger for Ni-Cd Batteries

This paper presents the development of an intelligent genetic algorithm (GA) technique for training of a generalized regression neural network (GRNN) controller to achieve a compact network and to decrease battery charging time on a cost-effective RISC microcontroller. The suitable input-output data were selected from GA mechanism to establish GRNN. The computational complexity of GRNN can be reduced replaced by some simple polynomial forms. As a consequence, the fast charging device for nickel-cadmium (Ni-Cd) batteries can be efficiently implemented on a low-cost 16F876A RISC microcontroller. Experimental results are shown to demonstrate superiority of the proposed system

Data selection of a compact GRNN for Ni-Cd batteries fast charging

This paper presents a data selection technique for training the neural network controller in order to archive a compact network and to decrease battery charging time. A fast-charging device for nickel-cadmium (Ni-Cd) batteries is designed through the generalized regression neural network (GRNN) and implemented with the MATLAB/SIMULINK for testing and operating on real system. The input-output data for training neural networks were collected from rigorous experimentation. The suitable data were selected to establish GRNN comprising only 13 processing elements. Experimental with real time implementation clearly show that the proposed technique not only requires less neural processing units but also yields less MSE than ANFIS and RBF technique.

Intelligent ultra fast charger for Ni-Cd batteries

This paper presents an intelligent technique for training the neural network controller in order to archive a compact network and to decrease battery charging time. An ultra fast charging device for nickel-cadmium (Ni-Cd) batteries is designed through the generalized regression neural network (GRNN) and implemented with the MATLAB/SIMULINK for testing and operating on real system. The input-output data for training neural networks were collected from rigorous experimentation. The suitable data were selected to establish GRNN comprising only 13 processing elements. Each node of the RBFs is an extendable support function which recovers the drawback of the existing compact support radial basis functions (CSRBF). Experiments with real time implementation clearly show that the proposed technique not only requires less neural processing units but also yields less MSE than the RBF technique.

Control of Bifurcation for a Current-mode DC/DC Boost Converter by Self-inductor Current Feedback

This paper presents the design of nonlinear dynamic control for switching a current-mode DC/DC boost converter by self-inductor current feedback. The proposed control algorithm is implemented on a SAB C167CR microcontroller to regulate the output voltage, provide an optimal of multiplied gain of inductor current feedback for keeping the system adequately remote from the first bifurcation. In spite of nonlinear characteristics and instabilities of the converter, the performance of the closed-loop control system can be considerably improved to avoid bifurcation phenomena. Experimental results show that the technique introduced in this paper gives satisfactory results with the regulating modes in various operating conditions