The expected lifetime performance and degradation of photovoltaic (PV) modules is a major issue facing the levelized cost of electricity of PV as a competitive energy source. Studies that quantify the rates and mechanisms of performance degradation are needed not only for bankability and adoption of these promising technologies, but also for the diagnosis and improvement of their mechanistic degradation pathways. Towards this goal, a generalizable approach to degradation science studies utilizing data science principles has been developed and applied to c-Si PV modules. By combining domain knowledge and data derived insights, mechanistic degradation pathways are indicated that link environmental stressors to the degradation of PV module performance characteristics. Targeted studies guided by these results have yielded predictive equations describing rates of degradation, and further studies are underway to achieve this for additional mechanistic pathways of interest.
Time-series insolation, environmental, thermal and power data were analyzed in a statistical analytical approach to identify the thermal performance of microinverters on dual-axis trackers under real-world operating conditions. This study analyzed 24 microinverters connected to 8 different brands of photovoltaic (PV) modules from July through October 2013 at the Solar Durability and Lifetime Extension (SDLE) SunFarm at Case Western Reserve University. Exploratory data analysis shows that the microinverter's temperature is strongly correlated with ambient temperature and PV module temperature, and moderately correlated with irradiance and AC power. Noontime data analysis reveals the variations of thermal behavior across different brands of PV module. Hierarchical clustering using the Euclidean distance measure principle was applied to noontime microinverter temperature data to group the similarly behaved microinverters. A multiple regression predictive model has been developed based on ambient temperature, PV module temperature, irradiance and AC power data to predict the microinverters temperature connected with different brands PV modules on dual-axis trackers.
A quantum computer based on the manipulation and detection of coherent states of electrons localized above a helium film is described. Each quantum logic element (qubit) is made of a combination of the ground and first excited states of an electron trapped in the image potential well at the surface. Potentials applied to micro-electrodes located beneath each electron confine the lateral motion of the electron and are used to operate gates. Mechanisms for one- and two-qubit logic operations and a readout of the final state are discussed. The principle decay channel for the excited state is via the emission of one phonon into the bulk liquid. Dechoherence times are calculated to be of order of 100 μs and allow greater than 104 serial operations.