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International Telemetering Conference Proceedings / October 30-November 02, 1995 / Riviera Hotel, Las Vegas, Nevada / The Stanford University Satellite Systems Development Laboratory will flight test a telemetry reengineering experiment on its student-built SAPPHIRE spacecraft. This experiment utilizes solar panel current information and knowledge of panel geometry in order to create a virtual sun sensor that can roughly determine the satellite's sun angle. The Omni-Directional Differential Sun Sensor (ODDSS) algorithm normalizes solar panel currents and differences them to create a quasi-linear signal over a particular sensing region. The specific configuration of the SAPPHIRE spacecraft permits the construction of 24 such regions. The algorithm will account for variations in panel outputs due to battery charging, seasonal fluctuations, solar cell degradation, and albedo affects. Operationally, ODDSS telemetry data will be verified through ground processing and comparison with data derived from SAPPHIRE's infrared sensors and digital camera. The expected sensing accuracy is seven degrees. This paper reviews current progress in the design and integration of the ODDSS algorithm through a discussion of the algorithm's strategy and a presentation of results from hardware testing and software simulation.

This thesis aims to analyze legislation on the management of electrical, electronic equipment and E-Waste, especially also solar panels. The object of the research is European legislation from its follows Czech legislation. The focus of the thesis is the chapter dealing with the life cycle of the electrical, electronic equipment and operators involved in the life cycle of electrical, electronic equipment. The main Act is Waste Act No. 185/2001 Coll. Powered by TCPDF (www.tcpdf.org)

The number of solar panels deployed worldwide has rapidly increased. Solar panels are often placed in areas not easily accessible. It is also difficult for panel owners to be aware of their operating condition. Many environmental factors have negative effects on the efficiency of solar panels. To reduce the power lost caused by environmental factors, it is necessary to detect and classify the anomalous events occurring on the surface of solar panels. This thesis designs and studies a device to continuously measure the voltage output of solar panels and to transmit the time series data back to a personal computer using wireless communication. A program was developed to store and model this time series data. It also detected the existence of anomalies and classified the anomalies by modeling the data. In total, ten types of anomalies were considered. These anomaly types include temporary shading, permanent shading, fallen leaves, accumulating snow and melting snow among others. Previous time series anomaly detection algorithms do not perform well for reallife situations and are only capable of dealing with at most four different types of anomalies. In this work, a general mathematical model is proposed to give better performance in real-life test cases and to cover more than four types of anomalies. We note that the models can be generalized to detect and to classify anomalies for general time series data which is not necessarily generated from solar panel. We compared several techniques to detect and to classify anomalies including the auto-regressive integrated moving average model (ARIMA), neural networks, support vector machines and k-nearest-neighbors classification. We found that anomaly classification using the k-nearest-neighbors classification was able to accurately detect and classify 97% of the anomalies in our test set. The devices and algorithms have been tested with two small 12-volt solar panels.

The aim of this study was to modify the platinum particle size to observe the effects on the efficiency of a Dye Sensitized Solar Cell (DSCC). DSSC was prepared as follows: On the anode side, TiO2 was annealed on the transparent conducting oxide (TCO) which is SnO2:F coated and a cis-bis (isothiocyanato) bis (2,2&#039 / -bipyridyl-4,4&#039 / -dicarboxylato)&ndash / ruthenium(II) dye was adsorbed on the TiO2. On the cathode side, platinum was coated on TCO from an alcohol based solution of platinum (plasitol) by thermal decomposition method. Potassium iodide and iodine were dissolved in ethylene glycol to prepare the electrolyte. Four cathode surfaces were prepared by thermal decomposition method at 400 oC and 5 min. Cathode surface morphology was changed by changing the annealing conditions. Current-voltage measurements were performed for determining the cell efficiency. One cathode glass was used as such giving a cell efficiency of 2.36%. Three glasses were further thermally treated at 450 oC, 500 oC and 550 oC for 30 min. highest efficiency was measured with the counter electrode annealed at 550 oC for 30 min as 2.89%. SEM micrographs of the substrate which was SnO2:F coated TCO revealed a decrease in average surface particle size with an increase in annealing temperature. EDX mappings showed that as the annealing temperature increased, Pt particles segregated together to form porous patches. In this study, it was demonstrated that as the annealing temperature of cathode increased, DSSC efficiency increased. These results can be used to design cheaper DSSCs with higher efficiencies.

The number of solar panels deployed worldwide has rapidly increased. Solar panels are often placed in areas not easily accessible. It is also difficult for panel owners to be aware of their operating condition. Many environmental factors have negative effects on the efficiency of solar panels. To reduce the power lost caused by environmental factors, it is necessary to detect and classify the anomalous events occurring on the surface of solar panels. This thesis designs and studies a device to continuously measure the voltage output of solar panels and to transmit the time series data back to a personal computer using wireless communication. A program was developed to store and model this time series data. It also detected the existence of anomalies and classified the anomalies by modeling the data. In total, ten types of anomalies were considered. These anomaly types include temporary shading, permanent shading, fallen leaves, accumulating snow and melting snow among others. Previous time series anomaly detection algorithms do not perform well for reallife situations and are only capable of dealing with at most four different types of anomalies. In this work, a general mathematical model is proposed to give better performance in real-life test cases and to cover more than four types of anomalies. We note that the models can be generalized to detect and to classify anomalies for general time series data which is not necessarily generated from solar panel. We compared several techniques to detect and to classify anomalies including the auto-regressive integrated moving average model (ARIMA), neural networks, support vector machines and k-nearest-neighbors classification. We found that anomaly classification using the k-nearest-neighbors classification was able to accurately detect and classify 97% of the anomalies in our test set. The devices and algorithms have been tested with two small 12-volt solar panels.

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.

Energy harvesting system is a system which could convert ambient energy into electrical power, its output depends on the energy availability of ambient conditions. For indoor condition, light is a typical available energy, and solar panel could be used to harvest it. To determine the light energy availability of an unknown condition, normally, a lot of measurements is needed, and it will cost a long time. This paper introduced a whole design process of a simulation system, it used modelling method to estimate the energy availability of unknown light condition, and this method is more quickly. A matched measurement system for solar panel and environmental parameters was built firstly. Then, all these environmental parameters were analyzed to find out their influence on solar panel. These parameters, which have relation to the output of the solar panel were thought as influence factors and used for model and classifier building. To get an accurate simulation result, different modeling and classification methods were compared and some suggested methods were picked out. Comparing the simulation result with the real output measurement, the method with minimum error was accepted in the final system. A user interface was built in the end to make this system become more user-friendly. This system could be used to simulate the energy availability of a new condition and analyze the error of simulation results which generated by different methods.

The restoring sources of energy and their development brings the question how these sources can be utilized in the best way. Master`s thesis deals with photovoltaic systems. It describes their history line, actual situation and also brings some expectations in the future. There are mentioned some materials and technologies which are used for its manufacture, conditions of using in practice, advantages and disadvantages and finally a real mobile application of photovoltaic which serves for a production of electricity that is saved in the battery.