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This project proposes a technique for preventing saturation in series transformers from dynamic voltage restorer (DVR) systems. The method consists in correcting the voltages which are injected through the transformers into the power system to compensate voltage sags. It restricts the compensating voltages during the sag whenever it predicts that a maximum limit for the flux linkage is about to be exceeded. The prediction is carried out at the beginning of a stabilized voltage sag. Moreover, the technique allows a certain level of sag compensation even when the estimated flux is expected to exceed the saturation limit. The voltage sag level and phase are computed through an adaptive recursive least squares (RLS). The RLS estimation incorporates a transient period before it achieves a stable state whenever there is a sag event. The DVR is not supposed to operate in this period. Therefore, this paper also outlines a simple procedure for detecting the RLS estimation stable level. Simulation of different scenarios of voltage sags and the results from the experimental implementation of a DVR show the effectiveness of the method. Fuzzy gains usually perform fine in the original system. This total project done in a MATLAB SIMULATION Software.
Keywords:
Dynamic voltage restorer (DVR), flux linkage, power quality (PQ), recursive least squares, saturation, transformer
Cite Article:
"Balanced Voltage Sag Correction Using Dynamic Voltage Restorer Based Fuzzy Polar Controller", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.1, Issue 2, page no.1-8, November-2016, Available :http://www.ijrti.org/papers/IJRTI1611001.pdf
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000204749
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator