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86 results for “Power Modeling”
Supplementary materials for manuscript "Which model features matter? An experimental approach to evaluate power market modeling choices"
<p>The folder "Output" includes:</p> <ul> <li>a subfolder "all" with all the outputs of the model runs</li> <li>a subfolder "Figures_for_manuscript" with the figures used in the manuscript</li> <li>several subfolders including a selection of model outputs involved in an experiment, with their plots.</li> </ul> <p>The folder "R scripts" includes several scripts. Use "main.R" to call the other scripts and functions. Edit the paths beforehand.</p>
Probabilistic Model-Based Diagnosis for Electrical Power Systems
We present in this article a case study of the probabilistic approach to model-based diagnosis. Here, the diagnosed system is a real-world electrical power system, namely the Advanced Diagnostic and Prognostic Testbed (ADAPT) located at the NASA Ames Research Center. Our probabilistic approach is formally well-founded, and based on Bayesian networks and arithmetic circuits. We pay special attention to meeting two of the main challenges model development and real-time reasoning often associated with real-world application of model-based diagnosis technologies. To address the challenge of model development, we develop a systematic approach to representing electrical power systems as Bayesian networks, supported by an easy-touse specication language. To address the real-time reasoning challenge, we compile Bayesian networks into arithmetic circuits. Arithmetic circuit evaluation supports real-time diagnosis by being predictable and fast. In experiments with the ADAPT Bayesian network, which contains 503 discrete nodes and 579 edges and produces accurate results, the time taken to compute the most probable explanation using arithmetic circuits has a mean of 0.2625 milliseconds and a standard deviation of 0.2028 milliseconds. In comparative experiments, we found that while the variable elimination and join tree propagation algorithms also perform very well in the ADAPT setting, arithmetic circuit evaluation was an order of magnitude or more faster. **Reference:** O. J. Mengshoel, M. Chavira, K. Cascio, S. Poll, A. Darwiche, and S. Uckun. "Probabilistic Model-Based Diagnosis: An Electrical Power System Case Study”. Accepted to IEEE Transactions on Systems, Man, and Cybernetics, Part A, 2009.
Prognostics Of Power Mosfets Under Thermal Stress Accelerated Aging Using Data-Driven And Model-Based Methodologies
An approach for predicting remaining useful life of power MOSFETs (metal oxide field effect transistor) devices has been developed. Power MOSFETs are semiconductor switching devices that are instrumental in electronics equipment such as those used in operation and control of modern aircraft and spacecraft. The MOSFETs examined here were aged under thermal overstress in a controlled experiment and continuous performance degradation data were collected from the accelerated aging experiment. Die-attach degradation was determined to be the primary failure mode. The collected run-to-failure data were analyzed and it was revealed that ON-state resistance increased as die-attach degraded under high thermal stresses. Results from finite element simulation analysis support the observations from the experimental data. Data-driven and model based prognostics algorithms were investigated where ON-state resistance was used as the primary precursor of failure feature. A Gaussian process regression algorithm was explored as an example for a data-driven technique and an extended Kalman filter and a particle filter were used as examples for model-based techniques. Both methods were able to provide valid results. Prognostic performance metrics were employed to evaluate and compare the algorithms.
Modeling SiO2 Ion Impurities Aging in Insulated Gate Power Devices Under Temperature and Voltage Stress
This paper presents a formal computational methodology to explain how the oxide in semiconductors degrades over time and the dependence of oxide degradation on voltage and temperature stresses. The effects of aging are modeled and quantified by modification of the gate-source capacitance value. The model output is validated using experimental results of a thermally aged power semiconductor device.
Towards Modeling the Effects of Lightning Injection on Power MOSFETs
Power electronics are widely used in critical roles in modern day aircrafts and hence their health management is of great interest. An important part of prognostics and health management of these devices is understand- ing the effect of high-stress events such as lightning and how they affect their aging. In this paper we present our study and analysis of lightning injection experiments with power MOSFETs in their ON state. We show the different kind of damages that can be caused by such events and analyze their effects on device performance parameters. In addition, we present a simple yet effec- tive modeling technique that can model the degradation in these devices. Such models will play a valuable role in understanding the behavior of these damaged devices when operated under normal conditions later and sub- sequently in prognosis of their remaining useful life.We present our results on the performance of this modeling and the scope within which they can be utilized for ac- curate estimation of device damage.
Prognostics of Power MOSFETs under Thermal Stress Accelerated Aging using Data-Driven and Model-Based Methodologies
An approach for predicting remaining useful life of power MOSFETs (metal oxide field effect transistor) devices has been developed. Power MOSFETs are semiconductor switching devices that are instrumental in electronics equipment such as those used in operation and control of modern aircraft and spacecraft. The MOSFETs examined here were aged under thermal overstress in a controlled experiment and continuous performance degradation data were collected from the accelerated aging experiment. Die- attach degradation was determined to be the primary failure mode. The collected run-to-failure data were analyzed and it was revealed that ON-state resistance increased as die-attach degraded under high thermal stresses. Results from finite element simulation analysis support the observations from the experimental data. Data-driven and model based prognostics algorithms were investigated where ON-state resistance was used as the primary precursor of failure feature. A Gaussian process regression algorithm was explored as an example for a data-driven technique and an extended Kalman filter and a particle filter were used as examples for model-based techniques. Both methods were able to provide valid results. Prognostic performance metrics were employed to evaluate and compare the algorithms.
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International Brain Laboratory public data
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OpenNeuro
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