Browsing by Subject "MPPT"
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Item Analysis of classical root-finding methods applied to digital maximum power point tracking for photovoltaic energy generation(2011-08) Chun, Seunghyun; Kwasinski, Alexis; Grady, William; Driga, Mircea; Hallock, Gary; Byoun, JaesooThis dissertation examines the application of various classical root finding methods to digital maximum power point tracking (DMPPT). An overview of root finding methods such as the Newton Raphson Method (NRM), Secant Method (SM), Bisection Method (BSM), Regula Falsi Method (RFM) and a proposed Modified Regula Falsi Method (MRFM) applied to photovoltaic (PV) applications is presented. These methods are compared among themselves. Some of their features are also compared with other commonly used maximum power point (MPP) tracking methods. Issues found when implementing these root finding methods based on continuous variables in a digital domain are explored. Some of these discussed issues include numerical stability, digital implementation of differential operators, and quantization error. Convergence speed is also explored. The analysis is used to provide practical insights into the design of a DMPPT based on classical root finding algorithms. A new DMPPT based on a MRFM is proposed and used as the basis for the discussion. It is shown that this proposed method is faster than the other discussed methods that ensure convergence to the MPP. The discussion is approached from a practical perspective and also includes theoretical analysis to support the observations. Extensive simulation and experimental results with hardware prototypes verify the analysis.Item MESH : a maximum power point tracker for a wireless sensor network(2010-12) Kobdish, Stephen Matthew; Aziz, Adnan; Aziz, AdnanEnergy harvesting is becoming increasingly important in low-power applications where energy from the environment is used to power the system alone, or to supplement a battery. For example, pulse oximeter sensors inside helmets of road racing cyclists are powered by the sun. These sensors have become smaller and more practical without the limitation of a finite energy supply. Harvested energy from an energy transducer (solar, piezoelectric, etc.) must be maximized to ensure these devices can survive periods where environmental energy is scarce. The conversion process from the transducer to usable power for the device is not perfectly efficient. Specifically, the output voltage of a solar cell is a function of the light intensity, and by extension the load it powers. A small perturbation of the light source quickly diminishes the available power. The wasted power reduces the energy available for the application, and can be improved using an approach called maximum power point tracking (MPPT). This technique maximizes harvesting efficiency by dynamically impedance matching the transducer to its load. This report introduces the Maximum Efficient Solar Harvester (MESH), an MPPT algorithm tuned for a specific Wireless Sensor Network (WSN) application. MESH specifically controls the operation of the DC-DC converter in a solar power management unit (PMU). The control is done by monitoring the available light and feeding that information to choose the optimal operating point DC-DC converter. This operating point has a direct dependency on the overall efficiency of the system. For MESH to be practical, the cost and power overhead of adding this functionality must be assessed. Empirical results indicate that MESH improves the maximum efficiency of the popular Texas Instruments (TI) RF2500-SEH WSN platform by an average of 20%, which far exceeds the power overhead it incurs. The cost is also found to be minimal, as WSN platforms already include a large portion of the hardware required to implement MESH. The report was done in collaboration with Shahil Rais. It covers the hardware components and the bench automation environment; Rais's companion report focuses on software implementation and MESH architecture definition.Item MESH : a power management system for a wireless sensor network(2010-12) Rais, Shahil Bin; Aziz, Adnan,Energy harvesting is becoming increasingly important in low-power applications where energy from the environment is used to power the system alone, or to supplement a battery. For example, pulse oximeter sensors inside helmets of road racing cyclists are powered by the sun. These sensors have become smaller and more practical without the limitation of a finite energy supply. Harvested energy from an energy transducer (solar, piezoelectric, etc.) must be maximized to ensure these devices can survive periods where environmental energy is scarce. The conversion process from the transducer to usable power for the device is not perfectly efficient. Specifically, the output voltage of a solar cell is a function of the light intensity, and by extension the load it powers. A small perturbation of the light source quickly diminishes the available power. The wasted power reduces the energy available for the application, and can be improved using an approach called maximum power point tracking (MPPT). This technique maximizes harvesting efficiency by dynamically impedance matching the transducer to its load. This report introduces the Maximum Efficient Solar Harvester (MESH), an MPPT algorithm tuned for a specific Wireless Sensor Network (WSN) application. MESH specifically controls the operation of the DC-DC converter in a solar power management unit (PMU). The control is done by monitoring the available light and feeding that information to choose the optimal operating point DC-DC converter. This operating point has a direct dependency on the overall efficiency of the system. For MESH to be practical, the cost and power overhead of adding this functionality must be assessed. Empirical results indicate that MESH improves the maximum efficiency of the popular Texas Instruments (TI) RF2500-SEH WSN platform by an average of 20%, which far exceeds the power overhead it incurs. The cost is also found to be minimal, as WSN platforms already include a large portion of the hardware required to implement MESH. The report was done in collaboration with Stephen Kobdish. It covers the software implementation and MESH architecture definition; Kobdish's companion report focuses on hardware components and the bench automation environment.