Dynamic energy management of energy harvesting wireless sensor nodes using fuzzy inference system with reinforcement learning

Cognitive Radio based Heterogeneous Wireless Sensor Network (CoRHAN) is an innovative multi-layered infrastructure approach in wireless engineering which incorporates different communication modes over large geographical area. CoRHAN employs cooperative communication among sensor nodes and cognitive radio to ensure an optimized communication experience for users. It shares radio resources fairly and efficiently by integrating multiple networks together. Challenge in such network is the ability to instantly detect interference on the frequencies being used and quickly tune to other better frequencies for communication reliability. In this paper, we have proposed an enhanced CoRHAN using Fuzzy Inference System (FIS). FIS is applied to mitigate the fading frequencies due to co-channel interference. It helps to sort out the best frequency channel among the selected cooperative spectrum sensed channels. Prototype was developed to demonstrate the proof of concept and analyze the feasibility and practicality of using FIS-CoRHAN technique in Cognitive Radio based Heterogeneous Wireless Sensor Network. Simulation results show that our solution achieves better performance when compared to existing CoRHAN approach substantially satisfying the robustness constraints.

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An Efficient Solar Energy Harvesting System for Wireless Sensor Nodes

2018 2nd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES) ◽

10.1109/icpeices.2018.8897434 ◽

2018 ◽

Cited By ~ 2

Author(s):

Himanshu Sharma ◽

Ahteshamul Haque ◽

Zainul Abdin Jaffery

Keyword(s):

Energy Harvesting ◽

Solar Energy ◽

Sensor Nodes ◽

Wireless Sensor ◽

Wireless Sensor Nodes ◽

Solar Energy Harvesting ◽

Energy Harvesting System

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Leveraging a Genetic Algorithm for the Optimal Placement of Distributed Generation and the Need for Energy Management Strategies Using a Fuzzy Inference System

Electronics ◽

10.3390/electronics10020172 ◽

2021 ◽

Vol 10 (2) ◽

pp. 172

Author(s):

Sunny Katyara ◽

Muhammad Fawad Shaikh ◽

Shoaib Shaikh ◽

Zahid Hussain Khand ◽

Lukasz Staszewski ◽

...

Keyword(s):

Genetic Algorithm ◽

Energy Management ◽

Fuzzy Inference System ◽

Distribution System ◽

Fuzzy Inference ◽

Management Strategies ◽

Test System ◽

Optimal Placement ◽

Inference System ◽

Simulated Environment

With the rising load demand and power losses, the equipment in the utility network often operates close to its marginal limits, creating a dire need for the installation of new Distributed Generators (DGs). Their proper placement is one of the prerequisites for fully achieving the benefits; otherwise, this may result in the worsening of their performance. This could even lead to further deterioration if an effective Energy Management System (EMS) is not installed. Firstly, addressing these issues, this research exploits a Genetic Algorithm (GA) for the proper placement of new DGs in a distribution system. This approach is based on the system losses, voltage profiles, and phase angle jump variations. Secondly, the energy management models are designed using a fuzzy inference system. The models are then analyzed under heavy loading and fault conditions. This research is conducted on a six bus radial test system in a simulated environment together with a real-time Power Hardware-In-the-Loop (PHIL) setup. It is concluded that the optimal placement of a 3.33 MVA synchronous DG is near the load center, and the robustness of the proposed EMS is proven by mitigating the distinct contingencies within the approximately 2.5 cycles of the operating period.

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