Tested Performance of Photovoltaic Array Optimization Technologies

Author(s):  
Aldrin J. Paynter ◽  
Mark Aschheim ◽  
Jorge E. Gonzalez

Growth in the solar industry continues as the cost of photovoltaic power is approaching grid parity. Several technologies have been developed that address reductions in power production associated with partial shading of panels, mismatch at the panel and string levels, and multiple array orientations, conditions that often arise in residential installations. In the absence of existing testing standards, a methodology was developed to evaluate performance of power optimization devices under shading conditions. Technologies investigated include buck-boost, parallel voltage boost, and impedance matching. Performance improvements range from negligible to a 44% increase in energy delivered under partial shading. Comparison data is presented along with guidance for selecting among the different technologies currently available on the market.

Author(s):  
Laura L. Liptai

Motorcyclists Suffer Serious Trauma More Often Than Automotive Occupants Tracing To Contact With Non-Yielding Road Surfaces And/Or Direct Impact From Other Vehicles. A Motorcycle Helmet Is The Principal Defense To Head Impact. If A Motorcycle Helmet Passes Dot, Department Of Transportation, Approval, What Performance Improvements Correlate? Dot And Non-Dot Helmets Were Tested To Determine Impact Performance At Velocities Exceeding Standardized Testing Velocities. Three Types Of Dot Approved And Three Types Of Non-Dot Approved Helmets Were Tested At Two Speeds Outside Of The Federal Testing Standards In The United States. The Analysis Was Performed Using An Inverted Pendulum Sub-System Experimental Device With A Hybrid-Iii Anthropometric Dummy Cranium And Neck. Results Quantify The Performance By Category, Model, And Experiment By Test Metric.


2014 ◽  
Vol 622 ◽  
pp. 141-145
Author(s):  
Govindaraju Rohini ◽  
V. Jamuna ◽  
D. Priyadarsini

This paper discuss about the effect of partial shading on photovoltaic array and driving the dc-dc boost converter to track maximum power point (MPP ) by incremental conductance (INC) MPPT algorithm. The temperature, irradiance, shading and array configuration will greatly affect the Photovoltaic performance. The shading effect on photovoltaic panel are caused by passing clouds ,neighbouring trees, neighbouring buildings ,towers .The PV characteristic of Photovoltaic panel get more complex under partial shading condition. The P-V and I-V characteristic under nonuniform insolation are simulated in Matlab based on solar irradiance and cell temperature. The design and analysis are made simple and easy through Simscape package.


Author(s):  
Lunde Ardhenta ◽  
Wijono Wijono

Wind energy and solar energy are the prime energy sources which are being utilized for renewal energy. The performance of a photovoltaic (PV) array for solar energy is affected by temperature, irradiation, shading, and array configuration. Often, the PV arrays are shadowed, completely or partially, by the passing clouds, neighboring buildings and towers, trees, and utility and telephone poles. Under partially shaded conditions, the PV characteristics are more complex with multiple peaks, hence, it is very important to understand and predict the MPP under PSC in order to extract the maximum possible power. This paper presents the development of PV array simulator for studying the I–V and P–V characteristics of a PV array under a partial shading condition. It can also be used for developing and evaluating new maximum power point tracking techniques, for PV array with partially shaded conditions. It is observed that, for a given number of PV modules, the array configuration significantly affects the maximum available power under partially shaded conditions. This is another aspect to which the developed tool can be applied. The model has been experimentally validated and the usefulness of this research is highlighted with the help of several illustrations


2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Jacob R Heldenbrand ◽  
Saurabh Baheti ◽  
Matthew A Bockol ◽  
Travis M Drucker ◽  
Steven N Hart ◽  
...  

Abstract Background Use of the Genome Analysis Toolkit (GATK) continues to be the standard practice in genomic variant calling in both research and the clinic. Recently the toolkit has been rapidly evolving. Significant computational performance improvements have been introduced in GATK3.8 through collaboration with Intel in 2017. The first release of GATK4 in early 2018 revealed rewrites in the code base, as the stepping stone toward a Spark implementation. As the software continues to be a moving target for optimal deployment in highly productive environments, we present a detailed analysis of these improvements, to help the community stay abreast with changes in performance. Results We re-evaluated multiple options, such as threading, parallel garbage collection, I/O options and data-level parallelization. Additionally, we considered the trade-offs of using GATK3.8 and GATK4. We found optimized parameter values that reduce the time of executing the best practices variant calling procedure by 29.3% for GATK3.8 and 16.9% for GATK4. Further speedups can be accomplished by splitting data for parallel analysis, resulting in run time of only a few hours on whole human genome sequenced to the depth of 20X, for both versions of GATK. Nonetheless, GATK4 is already much more cost-effective than GATK3.8. Thanks to significant rewrites of the algorithms, the same analysis can be run largely in a single-threaded fashion, allowing users to process multiple samples on the same CPU. Conclusions In time-sensitive situations, when a patient has a critical or rapidly developing condition, it is useful to minimize the time to process a single sample. In such cases we recommend using GATK3.8 by splitting the sample into chunks and computing across multiple nodes. The resultant walltime will be nnn.4 hours at the cost of $41.60 on 4 c5.18xlarge instances of Amazon Cloud. For cost-effectiveness of routine analyses or for large population studies, it is useful to maximize the number of samples processed per unit time. Thus we recommend GATK4, running multiple samples on one node. The total walltime will be ∼34.1 hours on 40 samples, with 1.18 samples processed per hour at the cost of $2.60 per sample on c5.18xlarge instance of Amazon Cloud.


2018 ◽  
Vol 153 ◽  
pp. 35-41 ◽  
Author(s):  
Chayut Tubniyom ◽  
Watcharin Jaideaw ◽  
Rongrit Chatthaworn ◽  
Amnart Suksri ◽  
Tanakorn Wongwuttanasatian

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