Comparative Study of Maximum Power Point Tracking with a Modified DC DC Converter

Maximum power points are used to find the voltages and currents at which a photovoltaic (PV) panel should operate to obtain maximum power. In order to deliver highest power, an efficient DC DC converter and a reliable tracking algorithm are used. There is also the need to continuously find the maximum power under any environmental conditions at all times. This research is intended to study a comparative performance of maximum power point (MPP) which is presented under uniform irradiance condition. The algorithm employed is an improved cuckoo search algorithm and the DC-DC Converter( switched mode power supply) has been modified by including a synchronous rectifier connected to a load, the performance of the system is validated using MATLAB/Simulink and practical implementation for this work. A comparison of the MATLAB Simulation with the practical implementation of MPP is presented using maximum power and percentage tracking efficiency as performance metric. From the MATLAB results obtained, maximum extracted power is 26.81W and the hardware implementation gives a maximum power of 28.71W. Tracking efficiency improves by 6.62%. The results show the practical MPP gives a better maximum power, which consequently improves the Photovoltaic systems efficiency and conversely mitigates the power consumption and the cost of the system than the simulation result obtained in MATLAB

Mitigating the Event and Effect of Energy Holes in Multi-hop Wireless Sensor Networks Using an Ultra-Low Power Wake-up Receiver and an Energy Scheduling Technique

This research work presents an algorithm for extending network lifetime in multi-hop wireless sensor networks (WSN). WSNs face energy gap issues around sink nodes due to the transmission of large amounts of data through nearby sensor nodes. The limited power supply to the nodes limits the lifetime of the network, which makes energy efficiency crucial. Multi-hop communication has been proposed as an efficient strategy, but its power consumption remains a research challenge. In this study, an algorithm is developed to mitigate energy holes around the sink nodes by using a modified ultra-low-power wake-up receiver and an energy scheduling technique. Efficient power scheduling reduces the power consumption of the relay node, and when the residual power of the sensor node falls below a defined threshold, the power emitters charge the nodes to eliminate energy-hole problems. The modified wake-up receiver improves sensor sensitivity while staying within the micro-power budget. This study's simulations showed that the developed RF energy harvesting algorithm outperformed previous work, achieving a 30% improvement in average charged energy (AEC), a 0.41% improvement in average energy (AEH), an 8.39% improvement in the number of energy transmitters, an 8.59% improvement in throughput, and a 0.19 decrease in outage probability compared to the existing network lifetime enhancement of multi-hop wireless sensor networks by RF Energy Harvesting algorithm. Overall, the enhanced power efficiency technique significantly improves the performance of WSNs

Energy-aware message distribution algorithm for enhance FANET pipeline surveillance reliability

Features such as the communication scheme, energy awareness, and task distribution amongst others are the key component that characterizes the Flying Ad-hoc Network (FANET). The operational efficiency in FANET surveying a specific region is affected by the nature of the UAVs' node placement, routing protocol, energy-aware task distribution, and node interaction amongst others. In this paper, Drone 1 (D1), Master Drone (DM), and Drone 2 (D2) were used to survey a pipeline of length 12.2 m. This paper aims at minimising energy use by drones during surveillance using energy-aware node exchange technique, task interaction and distribution scheme for each UAV. Due to fast energy depletion of DM due to packets aggregation, its election is based on the UAV with the highest energy before take-off. For two different simulations, 14,697.0 J and 14,836.6 J were obtained for DM. To avoid system failure due to fast energy loss of DM, the drones swapped positions and status. First swapping command comes up when DM loses 50% of its energy, while the second command occurs when it further loses 15%. Return to base threshold energy is computed for the three UAVs to avoid crash due to insufficient energy during surveillance. DM returns to base threshold energy for both single and double swapping simulation were 658.105 J and 652.456 J respectively. From the results obtained the algorithms were able to exchange nodes to maximize energy usage and perform an interaction-based task distribution for cooperative task sharing during surveillance. This translates into longer surveillance time and effective telemetry data aggregation