Data analytics and machine learning-based stability assessment of active grids
This project focuses on using Gaussian Process (GP) as a machine learning tool to solve Probabilistic Optimal Power Flow for systems with load uncertainties and renewable sources. It also tests the accuracy and competency of GP-POPF, by the use of different kernels, under the different number of...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| التنسيق: | Final Year Project |
| اللغة: | English |
| منشور في: |
Nanyang Technological University
2022
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/10356/157415 |
| الوسوم: |
إضافة وسم
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| الملخص: | This project focuses on using Gaussian Process (GP) as a machine learning tool to
solve Probabilistic Optimal Power Flow for systems with load uncertainties and
renewable sources. It also tests the accuracy and competency of
GP-POPF, by the use of different kernels, under the different number of bus systems. With
results obtained with the use of GP for POPF, they were compared to results obtained
from the traditional use of Monte-Carlo Simulations (MCS) with the purpose of
minimizing error measurements |
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