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Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM24698
Volume:
7 |
7 |
Jul 2023
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Voltage sag is one of the most severe power quality disturbances to be dealt with by the industrial sector, as it can cause severe process disruptions and results in substantial economic loss. A short-term decrease in voltage lasting anywhere from milliseconds up to a few seconds is called voltage sag. At present, a wide range of very flexible controllers, which capitalize on newly available power electronics components, are emerging for custom power applications. Among these, the distribution static compensator (D- STATCOM) and the dynamic voltage restorer (DVR) are most effective devices for mitigation of voltage sag, both of them based on the voltage source converter (VSC) principle. A DVR injects a voltage in series with the system voltage and a D-STATCOM injects a current into the system to correct the voltage sag. Control unit is the heart of DVR and D-STATCOM where the main function of it is to detect the presence of voltage sag in the system and calculating the required compensating voltage for the above compensating devices. Due to approximation in the process of the control design the existing control schemes used for DVR and D-STATCOM have some tracking error. This work evaluates the performance of the above compensating devices with a novel algorithm called two neuron control algorithm for simultaneous sag compensation and replenishing dc bus energy. This algorithm uses the simplest pre-sag supply voltage boosting technique. Besides, a self-charging technique is used which maintains the dc capacitor voltage at the desired level. The salient advantages of this method are compensating long duration deeper voltage sags, reduction in size of dc-link capacitor and simplicity of algorithm for digital implementation. Comprehensive results are presented to assess the performance of the two neuron control algorithm for each device as a potential custom power solution.
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Publication Date
7/18/2023
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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