Sponge Supercapacitor rule-based energy management strategy for wireless sensor nodes optimized by using dynamic programing algorithm

Liu, Xinzhi; Qi, Nanjian; Dai, Keren; Yin, Yajiang; Zhao, Jiahao; You, Zheng

doi:10.1016/j.energy.2021.122368

Cited by 17 publications

(9 citation statements)

References 33 publications

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“…Therefore, we applied a 3 kW PEMFC to guarantee system sustainability, and we set the PV modules in units of 1 kW and the battery modules in units of 48 V-100 Ah. These settings must be adjusted if the system's power level changes [30]. In addition, other components, such as supercapacitors [30], might also be integrated with the hybrid power systems.…”

Section: Discussionmentioning

confidence: 99%

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Extended-Window Algorithms for Model Prediction Applied to Hybrid Power Systems

Wang,

Huang

2024

Technologies

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This paper proposes extended-window algorithms for model prediction and applies them to optimize hybrid power systems. We consider a hybrid power system comprising solar panels, batteries, a fuel cell, and a chemical hydrogen generation system. The proposed algorithms enable the periodic updating of prediction models and corresponding changes in system parts and power management based on the accumulated data. We first develop a hybrid power model to evaluate system responses under different conditions. We then build prediction models using five artificial intelligence algorithms. Among them, the light gradient boosting machine and extreme gradient boosting methods achieve the highest accuracies for predicting solar radiation and load responses, respectively. Therefore, we apply these two models to forecast solar and load responses. Third, we introduce extended-window algorithms and investigate the effects of window sizes and replacement costs on system performance. The results show that the optimal window size is one week, and the system cost is 13.57% lower than the cost of the system that does not use the extended-window algorithms. The proposed method also tends to make fewer component replacements when the replacement cost increases. Finally, we design experiments to demonstrate the feasibility and effectiveness of systems using extended-window model prediction.

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Section: Discussionmentioning

confidence: 99%

“…These settings must be adjusted if the system's power level changes [30]. In addition, other components, such as supercapacitors [30], might also be integrated with the hybrid power systems. Finally, we have not considered battery degradation to simplify system designs.…”

Section: Discussionmentioning

confidence: 99%

Extended-Window Algorithms for Model Prediction Applied to Hybrid Power Systems

Wang,

Huang

2024

Technologies

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“…In an active mode, sensor nodes are used to actively measure and collect data from various sources, including physical processes, environmental sensors, and others. 23 This collected data can be used to optimize and automate the process, provide insights into operations, and aid in predictive maintenance. In addition, the collected data can be used to trigger alarms in the event of any abnormal activity.…”

Section: Node Level Optimizationmentioning

confidence: 99%

“…Active mode sensor nodes: Sensor nodes in industrial networks play a critical role in Industry 4.0, as they provide the necessary input and output into the automation process. In an active mode, sensor nodes are used to actively measure and collect data from various sources, including physical processes, environmental sensors, and others 23 . This collected data can be used to optimize and automate the process, provide insights into operations, and aid in predictive maintenance.…”

Section: Introductionmentioning

confidence: 99%

A novel energy optimization framework to enhance the performance of sensor nodes in Industry 4.0

Sivakumar,

Logeshwaran,

Kannadasan

et al. 2024

Energy Science & Engineering

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Industry 4.0 is a term used to refer to the fourth industrial revolution, characterized by the introduction of new technologies, such as the Internet of Things, Big Data, and artificial intelligence (AI). As the number of connected devices in industrial settings grows, energy optimization of such sensors becomes increasingly essential. This paper proposes an energy optimization framework for sensor nodes in Industry 4.0. The framework is based on energy efficiency, energy conservation, and energy harvesting principles. It is designed to optimize the energy consumption of sensor nodes while maintaining their performance. The framework includes dynamic power management, scheduling, and harvesting techniques to reduce energy consumption while maintaining performance. In addition, the framework provides a comprehensive approach to energy optimization, including advanced analytics and AI to predict energy consumption and optimize energy use. The proposed model reached 96.93% sensitivity, 91.36% false discovery rate, 11.28% false omission rate, 90.12% prevalence threshold, and 91.24% threat score. The proposed framework is expected to improve the performance of sensor nodes in Industry 4.0, enabling increased efficiency and cost savings.

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“…(27) Many dynamic algorithms to optimize the energy management strategy have been proposed. Liu et al (28) designed a sponge supercapacitor rule-based energy management strategy for WSNs, which was optimized using a dynamic programming algorithm. However, complex algorithms consume more power, making them less viable for small-scale energy harvesting.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Charging and Storage Design in Sustainable Solar-powered Wireless Sensor Node

Wang¹,

Li²,

Ma³

et al. 2022

Sensors and Materials

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In view of the large scale and distributed characteristics of transformers, which lead to inconvenient manual inspection and state monitoring, and the limited energy of batteries restricting the development of wireless sensor nodes (WSNs), WSNs powered by solar energy are a promising approach. We propose a power management circuit for dual energy storage and dual-channel charging of a supercapacitor and a lithium battery with four modes to deal with the different charging currents of photovoltaic power generation under strong and weak light illumination, as well as the time mismatch between the energy harvesting power and WSN consumption power. We designed a low-power WSN with temperature, humidity, vibration, and illumination sensors in a low-duty-cycle operation mode with an average current consumption of 4.96 mA. Our solar power management circuit with undervoltage lockout (UVLO), maximum power point tracking (MPPT), quick charging, and a sustainable output characteristic is designed by LTspice simulation and verified by experiment in alternating light outdoors. The experiment demonstrates sustainable autonomous wireless sensing by dual charging and the feasibility of our storage design for solar energy harvesting, indicating its potential application in grid transformers.

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Sponge Supercapacitor rule-based energy management strategy for wireless sensor nodes optimized by using dynamic programing algorithm

Cited by 17 publications

References 33 publications

Extended-Window Algorithms for Model Prediction Applied to Hybrid Power Systems

Extended-Window Algorithms for Model Prediction Applied to Hybrid Power Systems

A novel energy optimization framework to enhance the performance of sensor nodes in Industry 4.0

Hybrid Charging and Storage Design in Sustainable Solar-powered Wireless Sensor Node

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