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Leveraging a Dynamic Differential Annealed Optimization and Recalling Enhanced Recurrent Neural Network for Maximum Power Point Tracking in Wind Energy Conversion System

Leveraging a Dynamic Differential Annealed Optimization and Recalling Enhanced Recurrent Neural... In this article, a hybrid strategy is proposed to track that maximum power of Wind Energy Conversion System (WECS). The proposed technique is the joint execution of Dynamic Differential Annealed Optimization (DDAO) and Recalling Enhanced Recurrent Neural Network (RERNN) hence, it is called D2AORERN2 approach. In the proposed work, DDAO has input parameters, like rectifier outputs that means rectifier dc voltage, dc current, time. Based on input parameters, Dynamic Differential Annealed Optimization optimizes and minimizes the error in rectifier power and makes training dataset based on maximal power point tracking conditions. Based on accomplished dataset, the RERNN finds the optimal solution. The rectifier’s reference dc voltage is transformed for controlling the inverter switch pulses. Finally, the proposed method is activated in MATLAB/Simulink, its superiority is analyzed with various existing methods, like genetic algorithm (GA), particle swarm optimization (PSO), Hill Climb Search (HCS).The efficiency of proposed system is likened to several existing technique as GA, PSO and HCS. The efficiency of GA, PSO, HCS and proposed technique is 81%, 85%, 89% and99%. Thus the proposed technique achieves best result than the other techniques. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Technology and Economics of Smart Grids and Sustainable Energy Springer Journals

Leveraging a Dynamic Differential Annealed Optimization and Recalling Enhanced Recurrent Neural Network for Maximum Power Point Tracking in Wind Energy Conversion System

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Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022
eISSN
2199-4706
DOI
10.1007/s40866-022-00144-z
Publisher site
See Article on Publisher Site

Abstract

In this article, a hybrid strategy is proposed to track that maximum power of Wind Energy Conversion System (WECS). The proposed technique is the joint execution of Dynamic Differential Annealed Optimization (DDAO) and Recalling Enhanced Recurrent Neural Network (RERNN) hence, it is called D2AORERN2 approach. In the proposed work, DDAO has input parameters, like rectifier outputs that means rectifier dc voltage, dc current, time. Based on input parameters, Dynamic Differential Annealed Optimization optimizes and minimizes the error in rectifier power and makes training dataset based on maximal power point tracking conditions. Based on accomplished dataset, the RERNN finds the optimal solution. The rectifier’s reference dc voltage is transformed for controlling the inverter switch pulses. Finally, the proposed method is activated in MATLAB/Simulink, its superiority is analyzed with various existing methods, like genetic algorithm (GA), particle swarm optimization (PSO), Hill Climb Search (HCS).The efficiency of proposed system is likened to several existing technique as GA, PSO and HCS. The efficiency of GA, PSO, HCS and proposed technique is 81%, 85%, 89% and99%. Thus the proposed technique achieves best result than the other techniques.

Journal

Technology and Economics of Smart Grids and Sustainable EnergySpringer Journals

Published: Apr 29, 2022

Keywords: Maximum power; Wind energy conversion system; DC side voltage; Inverter switches; Control pulses

References