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Title

AMDCFELC: Design of an Adaptive Model for Duty-cycle Control with Fuzzy Rules for Improving Energy Harvesting Capabilities of Low Complexity Sensor Devices

Author

Jaya Dipti Lal, Dr Dolly Thankachan

Citation

Vol. 22  No. 6  pp. 697-705

Abstract

Performance of energy harvesting models has larger dependence on efficiency of sensors used for the harvesting process. Most of these models utilize Maximum Power Point Tracking (MPPT) to estimate optimum harvesting points for different energy sources. Thus, enhancing efficiency of MPPT based devices will directly improve harvesting efficiency of underlying sensor interfaces. To perform this task, a wide variety of models are proposed by researchers, and most of them have higher complexity, which reduces their energy efficiency under real-time use cases. Due to higher complexity of processing, most of the harvested energy is consumed during control operations, which limits performance of the underlying model under different scenarios. To overcome these limitations, this text proposes design of a novel Adaptive Model for Duty-cycle Control of MPPT devices via Fuzzy rules which assists in improving Energy harvesting capabilities of Low Complexity sensor devices. The proposed model initially uses a Q-Learning method for estimation of maximum energy harvesting points. Due to use of Q-Learning, the model is capable of incrementally estimating correct sensor positions with minimum energy consumption. This is followed by use of a fuzzy model, which assists in energy aware movements & reconfiguration of the harvesting sensor(s) for low complexity use cases. The fuzzy rule engine is built using a simplistic Mamdani model with pre-trained rules, which assists in identifying duty cycles for different MPPT devices for minimizing energy consumption under real-time use cases. Performance of this model was evaluated in terms of energy needed for harvesting, energy conserved during harvesting, delay needed for MPPT, and average power efficiency under different harvesting scenarios. This performance was compared with various state-of-the-art models, and it was observed that the propose model showcased 15.4% lower energy consumption for harvesting, 18.5% higher energy conservation, with 8.3% higher power efficiency, with 4.5% higher delay than other models..

Keywords

Energy, Harvesting, MPPT, Duty Cycle, Fuzzy, Rules, Low Complexity.

URL

http://paper.ijcsns.org/07_book/202206/20220688.pdf