Version 1
: Received: 27 February 2022 / Approved: 9 March 2022 / Online: 9 March 2022 (10:39:54 CET)
How to cite:
M.A.S. Mahmoud, M. Novel Design to Tune SVC for Power Factor Correction and Reactive Power Control Using Cascaded ANN Approach (CANNA). Preprints2022, 2022030132. https://doi.org/10.20944/preprints202203.0132.v1
M.A.S. Mahmoud, M. Novel Design to Tune SVC for Power Factor Correction and Reactive Power Control Using Cascaded ANN Approach (CANNA). Preprints 2022, 2022030132. https://doi.org/10.20944/preprints202203.0132.v1
M.A.S. Mahmoud, M. Novel Design to Tune SVC for Power Factor Correction and Reactive Power Control Using Cascaded ANN Approach (CANNA). Preprints2022, 2022030132. https://doi.org/10.20944/preprints202203.0132.v1
APA Style
M.A.S. Mahmoud, M. (2022). Novel Design to Tune SVC for Power Factor Correction and Reactive Power Control Using Cascaded ANN Approach (CANNA). Preprints. https://doi.org/10.20944/preprints202203.0132.v1
Chicago/Turabian Style
M.A.S. Mahmoud, M. 2022 "Novel Design to Tune SVC for Power Factor Correction and Reactive Power Control Using Cascaded ANN Approach (CANNA)" Preprints. https://doi.org/10.20944/preprints202203.0132.v1
Abstract
This article introduces new method using ANN to control SVC in order to correct the source power factor of low voltage network. Two ANN approaches are investigated to design accurate and fast power factor correction (PFC) controller. First approach uses simple ANN (SANNA). Second approach uses cascaded ANN (CANNA). ANN regression, performance and the calculated error for both approaches are investigated to select between the two approaches. CANNA is selected as a better solution and it used to build the ANN PFFC controller using database, generated by MATLAB-Simulink, for standard three low voltage levels (240V, 220V and 110V). Nine test cases are carried out to validate the performance of proposed ANN PFC controller with a network has variable loads and low power factor (0.6 approximately). In order to extend the use of the controller to other voltage levels not included in the training process of the cascaded ANN, only the SVC is resized. Another nine cases are carried out with the same loads using the same ANN controller, as it is, to test its performance with the extended voltage level range (415V, 230V and 120V). The results show accurate and fast response in all test cases.
Keywords
power system;reactive power control; power factor correction; arterial neural network
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
