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Smart Flow Management for Hybrid PV-Wind Connected Water Pumping Systems: Optimized UPFC and Fuzzy Q-Learning Control

International Journal of Electrical and Electronics Engineering
© 2024 by SSRG - IJEEE Journal
Volume 11 Issue 6
Year of Publication : 2024
Authors : P. Nagendra, K. Sasikala
10.14445/23488379/IJEEE-V11I6P131
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How to Cite?

P. Nagendra, K. Sasikala, "Smart Flow Management for Hybrid PV-Wind Connected Water Pumping Systems: Optimized UPFC and Fuzzy Q-Learning Control," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 6, pp. 305-316, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I6P131

Abstract:

Remote areas need to prioritize the pumping of water for agricultural and drinking purposes. Water pumping is a promising and advantageous application of renewable energy sources, such as hybrid energy sources that combine photovoltaic and wind power. A novel approach to effective flow control in hybrid PV and wind-powered water pumping systems is presented in this research. To optimize a Proportional Integral Derivative (PID) controller that governs motor speed, the proposed approach combines an optimized control algorithm, Unified Power Flow Controller (UPFC), and Fuzzy Q-learning. The main goal is to use UPFC to identify and mitigate Power Quality (PQ) issues. To achieve optimal UPFC operation under a range of solar and wind energy conditions, the Kookaburra Optimization Algorithm (KOA) is utilized. Fuzzy Q-learning provides adaptive parameter tuning based on real-time feedback and system dynamics, enabling the PID controller to be tuned online. Moreover, the detailed models of wind and photovoltaic systems highlight their influence on water pumping technology. A PID controller controlled by the KOA algorithm powers the UPFC, a crucial part of the system. The KOA is applied to optimize PID parameters for the UPFC, resulting in a unique contribution that emulates the hunting behavior of kookaburras for both local and global exploration. The PID controller can be easily adjusted online by the Fuzzy Q-Learning algorithm, which improves system stability when reacting to changing energy and environmental conditions. The combination of the Kookaburra Optimization Algorithm, PID control, and UPFC offers a sophisticated and flexible approach to the best possible operation of water pumping systems. Total Harmonic Distortion (THD) is successfully decreased by the proposed method, indicating better waveform quality and fewer harmonic components in the electrical system. The system’s ability to maintain a high-quality power supply is reflected in the THD value of 3.92% at 50Hz.

Keywords:

Water pumping systems, Unified Power Flow Controller (UPFC), Kookaburra Optimization Algorithm (KOA), PID controller and Fuzzy Q-learning.

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