PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
A Novel Adaptive Neuro-Fuzzy Based Cascaded PIDF-PIDF Controller for Automatic Generation Control Analysis of Multi-Area Multi-Source Hydrothermal System
Version 1
: Received: 17 November 2022 / Approved: 22 November 2022 / Online: 22 November 2022 (11:24:41 CET)
How to cite:
Ramshanker, A.; K., R.; Raglend, J.I.; Edward, B.J. A Novel Adaptive Neuro-Fuzzy Based Cascaded PIDF-PIDF Controller for Automatic Generation Control Analysis of Multi-Area Multi-Source Hydrothermal System. Preprints2022, 2022110422. https://doi.org/10.20944/preprints202211.0422.v1
Ramshanker, A.; K., R.; Raglend, J.I.; Edward, B.J. A Novel Adaptive Neuro-Fuzzy Based Cascaded PIDF-PIDF Controller for Automatic Generation Control Analysis of Multi-Area Multi-Source Hydrothermal System. Preprints 2022, 2022110422. https://doi.org/10.20944/preprints202211.0422.v1
Ramshanker, A.; K., R.; Raglend, J.I.; Edward, B.J. A Novel Adaptive Neuro-Fuzzy Based Cascaded PIDF-PIDF Controller for Automatic Generation Control Analysis of Multi-Area Multi-Source Hydrothermal System. Preprints2022, 2022110422. https://doi.org/10.20944/preprints202211.0422.v1
APA Style
Ramshanker, A., K., R., Raglend, J.I., & Edward, B.J. (2022). A Novel Adaptive Neuro-Fuzzy Based Cascaded PIDF-PIDF Controller for Automatic Generation Control Analysis of Multi-Area Multi-Source Hydrothermal System. Preprints. https://doi.org/10.20944/preprints202211.0422.v1
Chicago/Turabian Style
Ramshanker, A., Jacob I. Raglend and Belwin J. Edward. 2022 "A Novel Adaptive Neuro-Fuzzy Based Cascaded PIDF-PIDF Controller for Automatic Generation Control Analysis of Multi-Area Multi-Source Hydrothermal System" Preprints. https://doi.org/10.20944/preprints202211.0422.v1
Abstract
This article investigated the Automatic Generation Control(AGC) of multi-area multi-source interconnected systems with hydropower plants, thermal power plants, and wind energy. Adaptive Neuro-fuzzy controller integrated with the cascaded proportional-integral-derivative with filter (PIDF-PIDF) is a new cascaded controller (ANF-PIDF-PIDF) that has been presented as a secondary controller for applied hybrid power systems. The recent Skill Optimization Algorithm (SOA) is employed to optimize PIDF- PIDF controller parameter gains and the Adaptive Neuro-Fuzzy controller's inputs and output scaling factors. SOA is used to update the controller parameters with integral square error (ISE) employed as the objective function. A 1% step load disturbance was considered simultaneously in all three areas. The controller's performance is evaluated and compared with and without considering the effects of wind energy sources and non-linearity for ANF-PIDF-PIDF, PIDF-PIDF, and PIDF and it was determined that the ANF-PIDF-PIDF was the most efficient. The dynamic system performance is also compared with parallel high voltage direct current (HVDC) tie-lines. The investigation clearly shows that incorporating HVDC tie-line with multi-area, multi-source provides better dynamic performance in maximum amplitude, oscillation, and settling time. Additionally, sensitivity analysis is done and the optimum controller gains does not need to be reset to uncertain values in system loading conditions. All simulation results were evaluated using MATLAB 2016b.
Keywords
Automatic generation controls (AGC); Adaptive Neuro-Fuzzy controller; cascaded controller; parallel High voltage direct current (HVDC) tie-lines; Skill Optimization Algorithm (SOA)
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.
