Wahyu Mulyo Utomo

Solar Tracker Robot using microcontroller

Shortage of non-renewable fuel in the future is an upcoming global issue; therefore renewable energy such as solar energy has gradually replaced non-renewable energy. However, the output power provided via the photovoltaic conversion process depends on solar irradiation. The objective of this project is to design and develop an automatic Solar Tracker Robot (STR) which is capable to track maximum light intensity. The efficiency of the solar energy conversion can be optimized by receiving maximum light on the solar panel. The main components of the robot consist of microcontroller namely PIC16F877A, sensors, servo motors and digital compass. This robot is programmed to detect sunlight by using two Light Dependent Resistors (LDR). Servo motor aligns the solar panel to receive maximum light. Digital compass is used to detect the position of the robot. Two modified DC servo motors will move the robot back to the original position once the robot is out of position. The robot is programmed using MPLAB IDE v8.30. Analysis on the power conversion of solar panel is being carried out by using Fluke 1750 power quality recorder.

Efficiency Optimization of Variable Speed Induction Motor Drive Using Online Backpropagation

Development of an efficiency optimization control for variable speed drive system is important not only from the viewpoints of energy saving but also from the broad perspective of greenhouse emission. In this paper the designs of a back propagation based efficiency optimization control (BPEOC) for variable speed compressor induction motor drive is described. The controller is designed to generate signal voltage and frequency references simultaneously. This technique allows for control of both the speed and efficiency. In order to achieve a robust BPEOC from variation of motor parameters, an online learning algorithm is employed. Simulation of the BPEOC and laboratory experimental set up has been developed using TMS320C60 digital signal processor. The result demonstrated a significant increase in efficiency and an improvement in speed performance.

Online Learning Neural Network Control of Buck-Boost Converter

This paper proposes a neural network control scheme of a DC-DC buck-boost converter using online learning method. In this technique, a back propagation algorithm is derived. The controller is designed to stabilize the output voltage of the DC-DC converter and to improve performance of the Buck-Boost converter during transient operations. Furthermore, to investigate the effectiveness of the proposed controller, some operations such as starting-up and reference voltage variations are verified. The numerical simulation results show that the proposed controller has a better performance compare to the conventional PI-Controller method.

FPGA implementation of Unipolar SPWM for single phase inverter

Nowadays, inverter which is also known as a DC-AC converter is one of the most popular parts in electrical devices that converts direct (DC) current to alternating current (AC) at the desired output voltage and frequency. This project presents development of a Unipolar Sinusoidal Pulse Width Modulation (SPWM) for single phase full bridge inverter using Field Programmable Logic Array (FPGA). The Unipolar SPWM is employed to control the output voltage magnitude of the inverter. Altera DE2–70 board with 16 bit serial configuration devices is used as a controller for the implementation of the SPWM inverter. Computation of the turn-on and turn-off times of the SPWM is developed using Matlab. To verify the proposed FPGA-SPWM, voltage amplitude regulations are carried.

On line optimal control of variable speed compressor motor drive system using neural control model

One of the methods used to increase energy savings in air conditioning system is by varying the speed of the motor compressor. A neural network control based on scalar control model for variable speed induction motor compressor is proposed. The controller is used to generate the signals of the voltage and frequency reference control. This system can be used to minimize the input power required to the motor drive in steady state and transient change conditions. The proposed method is tested by MATLAB simulation.

Neuro-Fuzzy On-Line Optimal Energy Control For Variable Speed Compressor Motor Drive System

In this paper the design of neuro-fuzzy optimal controller for induction motor drive is described. A controller is applied to optimize the power consumption of a variable speed compressor motor drive system. Based on the scalar control method the controller is designed to generate an optimum volt per hertz reference. This controller consists of adaptive fuzzy logic control in which the parameters of the controller updated by an online back propagation algorithm. The simulation and experimental result demonstrated that the proposed method can increase energy saving of a variable speed compressor motor drive system.

Vector Control of Induction Motor Using Neural Network

This paper deals with field oriented control of induction motor drive system with an online neural network for speed control. The field oriented control used to decoupling the flux and torque in order to get the performance as well as direct current motor. The online neural network is designed to maintain the output speed variation. To verify the effectiveness of the proposed method, a simulation model was developed. The result shows that the performance of transient response is improved in term of overshoot and settling time by using neural network field oriented control system. It is concluded that neural network based field oriented control schemes of induction motor drive is more effective to replace the conventional proportional integral derivative based field oriented control technique.

Design investigation of three phase HEFSM with outer-rotor configuration

Flux switching machines (FSMs) with the advantages of single piece of rotor and robust rotor structure become a popular research topic in recent years that suitable for various speed conditions. This machine combined the advantages of Switched Reluctance Machines (SRMs) and Permanent Magnet Synchronous Machines (PMSMs). Previously, most of developed FSMs are mainly focused on inner-rotor configuration, and hard to find research on outer-rotor FSM configuration. Therefore, this paper presents design investigation of three phase Hybrid Excitation Flux Switching Machine (HEFSM) with outer-rotor configuration. Primarily, design processes of the outer-rotor HEFSM especially for drawing parts, materials and conditions used, and properties setting are described in detail. Next, coil arrangement tests are also evaluated to confirm the principle operation of the machine and the phase sequence of each armature coil. Finally, DC field excitation flux capabilities at various current density condition, flux path and flux distribution, back-emf voltage and initial torque are also investigated.

Artificial neural network controller for single-phase active power filter

This paper shows the conception and simulation of a single-phase shunt active power filter for harmonics and power factor compensation of uncontrolled rectifier with battery load. The objectives of this project are to minimize the harmonic distortion and intensity the power factor of the single-phase system with battery charger load. The design of shunt active power filter is verified using the simulations in Matlab/Simulink. Shunt active harmonic filter was controlled by the neural network method where it applied the proportional and integral method in order to adjust the direct current link voltage and the hysteresis current controller is employed to generate signals for switching the voltage source inverter. The error signal has been compensated using the controllers. The reference filter currents signal is obtained by subtracting the line current with the compensated signal from the controller. This reference current is fed to the hysteresis current controller and compare with the sensed filter currents to obtain the switching signal for active power filter. Simulation results are acquired with the conventional PI controller and ANN controller.

Neural Network Efficiency Optimization Control of A Variable Speed Compressor Motor Drive

This paper presents a new method of improving the energy efficiency of a variable speed drive (VSD) for induction motors. The efficiency of induction motor at low load operation and at rated flux is very low compared when operating at full load due to excessively increase in iron losses. To improve this efficiency, it is essential to obtain the flux level that minimizes the total motor losses. In this paper, the proposed controller is designed to generate both the voltage and frequency reference signals simultaneously. The proposed controller was simulated for variable speed compressor application. The results obtained clearly show that the efficiency at low speed is significantly increased. Besides that the speed of the motor can be maintained. The simulation results are also verified by experiment.