Adaptive Rectifier Driven by Power Intake Predictors for Wind Energy Harvesting Sensor Networks

This paper presents a power management technique for improving the efficiency of harvesting energy from air-flows in wireless sensor networks (WSNs) applications. The proposed architecture consists of a two-stage energy conversion circuit: an ac-dc converter followed by a dc-dc buck-boost regulator with maximum power point tracking capability. The key feature of the proposed solution is the adaptive hybrid voltage rectifier, which exploits both passive and active topologies combined with power prediction algorithms. The adaptive converter significantly outperforms other solutions, increasing the efficiency between 10% and 30% with respect to the only passive and the only active topologies. To assess the performance of this approach in a reallife scenario, air-flow data have been collected by deploying WSN nodes interfaced with a wind microturbine in an underground tunnel of the Metro B1 line in Rome. It is shown that, using the adaptive ac-dc converter combined with power prediction algorithms, nodes deployed in the tunnel can harvest up to 22% more energy with respect to previous methods. Finally, it is shown that using power management techniques optimized for the specific scenario, the overall system overhead, in terms of average number of sampling performed per day by a node, is reduced of up to 93%.