| 1. |
- Calil, Wílerson Venceslau, et al.
(författare)
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Determining total cost of ownership and peak efficiency index of dynamically rated transformer at the PV-power plant
- 2024
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Ingår i: Electric power systems research. - : Elsevier BV. - 0378-7796 .- 1873-2046. ; 229
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Tidskriftsartikel (refereegranskat)abstract
- Dynamic rating of the transformer is a promising technology, which is suitable for various applications. Using dynamic rating for connecting renewable energy is believed to be beneficial for the economy and flexibility of the power system. However, to safely deploy such operation strategies, it is important to have more precise estimates for the total costs of owning such units and determine how effective such operation method is for a solar power plant. This study proposes a method for calculating total ownership costs (TOC) of dynamically rated transformers used for the connection of the solar power plant to the grid as well as analyzes its efficiency. The sensitivity analysis looks into the change in TOC and peak efficiency index (PEI) after considering reactive power dispatch. Results of this study also show how TOC, PEI, and load and no-load losses change depending on the transformer size.
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| 2. |
- Nagata, Erick A., et al.
(författare)
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Real-Time Voltage Sag Detection and Classification for Power Quality Diagnostics
- 2020
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Ingår i: Measurement. - : Elsevier. - 0263-2241 .- 1873-412X. ; 164
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Tidskriftsartikel (refereegranskat)abstract
- This work proposes an innovative approach to detect, segment and classify voltage sags according to their causes. To detect and segment, Independent Component Analysis is used, with the advantage of being fast and with low computational effort in the operational stage, once it uses only 1/8 cycle of the fundamental component. For classification purposes, Higher-Order Statistics are used for feature extraction and the classifiers are based on Neural Networks and Support Vector Machines. It was tested signal windows of 1, 1/2, 1/4 and 1/8 cycle. For both detection/segmentation design and feature selection, it was used the metaheuristics Teaching-Learning-Based Optimization. Encouraging results were achieved for the simulated signals. In addition, real signals were used to evaluate the detection and segmentation method and good results were achieved in which a detection error rate of 0.86% was achieved.
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