Solar energy is the energy derived from the sun through the form of solar radiation. Solar powered electrical generation relies on photovoltaic (PV) systems and heat engines. These two technologies are widely used today to provide power to either standalone loads or for connection to the power system grid. Maximum power point tracking (MPPT) is an essential part of a PV system. This is needed in order to extract maximum power output from a PV array under varying atmospheric conditions to maximize the return on initial investments. As such, many MPPT methods have been developed and implemented including perturb and observe (P&O), incremental conductance (IC) and Neural Network (NN) based algorithms. Judging between these techniques is based on their speed of locating the maximum power point (MPP) of a PV array under given atmospheric conditions, besides the cost and complexity of implementing them. The P&O and IC algorithms have a low implementation complexity but their tracking speed is sluggish. NN based techniques are faster than P&O and IC. However, they may not provide the global optimal point since they are prone to multiple local minima. To overcome the demerits of the aforementioned methods, support vector regression (SVR) based strategies have been proposed for the estimation of solar irradiation (for MPPT). A significant advantage of SVR based strategies is that it can provide the global optimal point, unlike NN based methods. In the published literature of SVR based MPPT algorithms, however, researchers have assumed a constant temperature. The assumption is not plausible in practice as the temperature can vary significantly during the day. The temperature variation, in turn, can remarkably affect the effectiveness of the MPPT process; the inclusion of temperature measurements in the process will add to the cost and complexity of the overall PV system, and it will also reduce the reliability of the system. The main goal of this thesis is to present a novel sensorless SVR based multi-stage algorithm (MSA) for MPPT in PV systems. The proposed algorithm avoids outdoor irradiation and temperature sensors. The proposed MSA consists of three stages: The first stage estimates the initial values of irradiation and temperature; the second stage instantaneously estimates the irradiation with the assumption that the temperature is constant over one-hour time intervals; the third stage updates the estimated value of the temperature once every one hour. After estimating the irradiation and temperature, the voltage corresponding to the MPP is estimated, as well. Then, the reference PV voltage is given to the power electronics interface. The proposed strategy is robust to rapid changes in solar irradiation and load, and it is also insensitive to ambient temperature variations. Simulations studies in PSCAD/EMTDC and Matlab demonstrate the effectiveness of the proposed technique.

Solar energy is the energy derived from the sun through the form of solar radiation. Solar powered electrical generation relies on photovoltaic (PV) systems and heat engines. These two technologies are widely used today to provide power to either standalone loads or for connection to the power system grid. Maximum power point tracking (MPPT) is an essential part of a PV system. This is needed in order to extract maximum power output from a PV array under varying atmospheric conditions to maximize the return on initial investments. As such, many MPPT methods have been developed and implemented including perturb and observe (P&O), incremental conductance (IC) and Neural Network (NN) based algorithms. Judging between these techniques is based on their speed of locating the maximum power point (MPP) of a PV array under given atmospheric conditions, besides the cost and complexity of implementing them. The P&O and IC algorithms have a low implementation complexity but their tracking speed is sluggish. NN based techniques are faster than P&O and IC. However, they may not provide the global optimal point since they are prone to multiple local minima. To overcome the demerits of the aforementioned methods, support vector regression (SVR) based strategies have been proposed for the estimation of solar irradiation (for MPPT). A significant advantage of SVR based strategies is that it can provide the global optimal point, unlike NN based methods. In the published literature of SVR based MPPT algorithms, however, researchers have assumed a constant temperature. The assumption is not plausible in practice as the temperature can vary significantly during the day. The temperature variation, in turn, can remarkably affect the effectiveness of the MPPT process; the inclusion of temperature measurements in the process will add to the cost and complexity of the overall PV system, and it will also reduce the reliability of the system. The main goal of this thesis is to present a novel sensorless SVR based multi-stage algorithm (MSA) for MPPT in PV systems. The proposed algorithm avoids outdoor irradiation and temperature sensors. The proposed MSA consists of three stages: The first stage estimates the initial values of irradiation and temperature; the second stage instantaneously estimates the irradiation with the assumption that the temperature is constant over one-hour time intervals; the third stage updates the estimated value of the temperature once every one hour. After estimating the irradiation and temperature, the voltage corresponding to the MPP is estimated, as well. Then, the reference PV voltage is given to the power electronics interface. The proposed strategy is robust to rapid changes in solar irradiation and load, and it is also insensitive to ambient temperature variations. Simulations studies in PSCAD/EMTDC and Matlab demonstrate the effectiveness of the proposed technique.

This thesis proposes a new maximum power point tracking (MPPT) method for photovoltaic (PV) systems using Kalman filter. The Perturbation & Observation (P&O) method is widely used due to its easy implementation and simplicity. The P&O usually requires a dithering scheme to reduce noise effects, but the dithering scheme slows the tracking response time. Tracking speed is the most important factor for improving efficiency under frequent environmental change. The proposed method is based on the Kalman filter. An adaptive MPPT algorithm which uses an instantaneous power slope has introduced, but process and sensor noises disturb its estimations. Thus, applying the Kalman filter to the adaptive algorithm is able to reduce tracking failures by the noises. It also keeps fast tracking performance of the adaptive algorithm, so that enables using the Kalman filter to generate more powers under rapid weather changes than using the P&O. For simulations, a PV system is introduced with a 30kW array and MPPT controller designs using the Kalman filter and P&O. Simulation results are provided the comparison of the proposed method and the P&O on transient response for sudden system restart and irradiation changes in different noise levels. The simulations are also performed using real irradiance data for two entire days, one day is smooth irradiance changes and the other day is severe irradiance changes. The proposed method has showed the better performance when the irradiance is severely fluctuating than the P&O while the two methods have showed the similar performances on the smooth irradiance changes. / Master of Science

Master of Science / Department of Mechanical and Nuclear Engineering / Guoqiang Hu / In some types of renewable energy systems such as wind turbines or solar power plants, the optimal operating conditions are influenced by the intermittent nature of these energies. This fact, along with the modeling difficulties of such systems, provides incentive to look for non-model based adaptive techniques to address the maximum power point tracking (MPPT) problem. In this thesis, a novel extremum seeking algorithm is proposed for systems where the optimal point and the optimal value of the cost function are allowed to be time varying. A sinusoidal perturbation based technique is used to estimate the gradient of the cost function. Afterwards, a robust optimization method is developed to drive the system to its optimal point. Since this method does not require any knowledge about the dynamic system or the structure of the input-to-output mapping, it is considered to be a non-model based adaptive technique. The proposed method is then employed for maximizing the energy capture from the wind in a variable speed wind turbine. It is shown that without any measurements of wind velocity or power, the proposed method can drive the wind turbine to the optimal operating point. The generated power is observed to be very close to the maximum possible values.

It is essential to always track maximum power from photovoltaic (PV) sources. Failure to track the global maximum power point under partial shading conditions is one of the major reasons that lead to significant power losses. Several maximum power point tracking methods have been proposed to deal with this problem. However, none of them were able to effectively identify the occurrence of partial shading. With the facility of Matlab modelling and simulation as well as the aid of a constructed solar emulator, the power-voltage characteristics of a PV panel under uniform and non-uniform irradiance conditions have been studied and some useful conclusions have been identified from observation. Based on these conclusions, a novel maximum power point tracking algorithm has been proposed, which is capable of identifying the occurrence of partial shading hence determining the need for a global scan over the operation range of PV panels for the true maximum power point. In the meantime, the effect of PV dynamics, due to the capacitance of PV cells, on maximum power point trackers has been investigated and some initial results and suggestions have been presented in this work.

Η παρούσα διπλωματική εργασία πραγματεύεται την ανάλυση και κατασκευή ενός αυτόνομου φωτοβολταϊκού συστήματος, το οποίο περιλαμβάνει αντλία. Ταυτόχρονα διενεργείται μελέτη ώστε το σύστημα να λειτουργεί στο σημείο μέγιστης αποδιδόμενης ισχύος. Η εργασία αυτή εκπονήθηκε στο Εργαστήριο Ηλεκτρομηχανικής Μετατροπής Ενέργειας του Τμήματος Ηλεκτρολόγων Μηχανικών και Τεχνολογίας Υπολογιστών της Πολυτεχνικής Σχολής του Πανεπιστημίου Πατρών. Σκοπός είναι η οδήγηση αντλίας χαμηλής ισχύος από φωτοβολταϊκό σύστημα, αξιοποιώντας όσο το δυνατόν πιο αποτελεσματικά την διαθέσιμη ηλιακή ακτινοβολία. Για να συμβεί αυτό παρεμβάλλεται μεταξύ των δύο στοιχείων μετατροπέας συνεχούς τάσης. Παράλληλα υλοποιείται κύκλωμα ελέγχου, ικανό να οδηγήσει το σύστημα στο μέγιστο σημείο ισχύος μέσω μεταβολής του λόγου κατάτμησης του μετατροπέα. Αρχικά αναλύεται το φωτοβολταϊκό φαινόμενο. Σαν αποτέλεσμα αυτής της ανάλυσης προκύπτει το ηλεκτρικό ισοδύναμο ενός φωτοβολταϊκού πίνακα, ο οποίος αποτελεί την πηγή ισχύος του συστήματος. Αναπτύσσεται το αντίστοιχο μοντέλο σε προγραμματιστικό περιβάλλον, το οποίο προσαρμόζεται ώστε τα χαρακτηριστικά του να αναπαριστούν πραγματικό πίνακα. Στη συνέχεια διερευνάται ο τρόπος που αλληλεπιδρά η πηγή ισχύος όταν συνδέεται σε φορτίο. Σύμφωνα με τα αποτελέσματα επιλέγεται το είδος του μετατροπέα που θα χρησιμοποιηθεί. Ακολουθεί η διαστασιολόγηση του μετατροπέα και η μοντελοποίηση του. Το επόμενο βήμα αποτελείται από την ανάλυση του κυκλώματος ελέγχου του συστήματος καθώς και τη μοντελοποίηση μηχανής συνεχούς ρεύματος συνδεδεμένη ως αντλία. Κατόπιν συνδέονται όλα τα μοντέλα και εξετάζεται η συνολική συμπεριφορά του συστήματος σε περιβάλλον SIMULINK. Τέλος κατασκευάζονται ο μετατροπέας και το κύκλωμα ελέγχου και αξιολογείται η συμπεριφορά τους βάσει πειράματος σε εργαστηριακές συνθήκες. / --

Gato del Sol III, was powered by a solar array of 480 Silicon mono-crystalline photovoltaic cells. Maximum Power Point trackers efficiently made use of these cells and tracked the optimal load. The cells were mounted on a fiber glass and foam core composite shell. The shell rides on a lightweight aluminum space frame chassis, which is powered by a 95% efficient brushless DC motor. Gato del Sol IV was the University of Kentucky Solar Car Team’s (UKSCT) entry into the American Solar Car Challenge (ASC) 2010 event. The car makes use of 310 high density lithium-polymer batteries to account for a 5 kWh pack, enough to travel over 75 miles at 40 mph without power generated by the array. An in-house battery protection system and charge balancing system ensure safe and efficient use of the batteries. Various electrical sub-systems on the car communicate among each other via Controller Area Network (CAN). This real time data is then transmitted to an external computer via RF communication for data collection.

Recently, photovoltaic (PV) generation is becoming increasingly popular in industrial applications. As a renewable and alternative source of energy they feature superior characteristics such as being clean and silent along with less maintenance problems compared to other sources of the energy. In PV generation, employing a Maximum Power Point Tracking (MPPT) method is essential to obtain the maximum available solar energy. Among several proposed MPPT techniques, the Perturbation and Observation (P&O) and Model Predictive Control (MPC) methods are adopted in this work. The components of the MPPT control system which are P&O and MPC algorithms, PV module and high gain DC-DC boost converter are simulated in MATLAB Simulink. They are evaluated theoretically under rapidly and slowly changing of solar irradiation and temperature and their performance is shown by the simulation results, finally a comprehensive comparison is presented.

The aim of this thesis is to familiarize with the issue of registration of medical image sequences. The main objective was to focus on the method of feature tracking in the image and various options of its implementation. The theoretical part describes various methods for detection of feature points and future point matching methods. In the practical part these methods were implemented in Matlab programming environment and a simple graphical user interface was created.

A module-level DC optimization proof of concept architecture is proposed to increase the efficiency of photovoltaic (PV) strings by minimizing the negative effects of shading caused by intermittent cloud cover while reducing cloud induced fast frequency fluctuations. The decentralized inverter approach combines the benefits of string and micro-inverter technology. This device can be affixed to pre-existing or new systems and operates in compliance with IEEE 1547 and California rule 21 standards by operating in maximum power point tracking (MPPT) or curtailment mode whenever necessary. The modular level device encapsulates three individual processes: an optimization engine to determine minimum power requirements, a fuzzy logic controller (FLC) to eliminate the effect of passing cloud cover, and a voltage regulation stage to monitor and appropriately adjust the output voltage of the device. Ramp rate reduction was accomplished using adaptive fuzzy logic control with a heuristic rule base inference engine. The modular design can be affixed to grid connected or islanded systems allowing for operation in regulated and variable load conditions. Matlab/Simulink 2019a was used to design and simulate the proof of concept model to verify the resiliency to partial shading, reduction of ramp rates during passing cloud coverage, and optimal output voltage for each panel while maintaining a constant DC link voltage of 120 V. This proof of concept has been successfully validated therefore further testing will be performed for various irradiance conditions.