Hybrid Energy Harvesting System Optimization in the Oil and Gas Industry via Harris Hawks Optimization Technique

In recent years, sustainable green energy harvesting systems have gained considerable attention for their potential use in self-powered smart Wireless Sensor Network (WSN) systems. The significance of these energy harvester systems is that they can extract pollution-free energy from the surrounding environment, thus eliminating the need for power connections. The existing research reveals that by employing piezoelectric and thermoelectric elements, the current technologies can extract piezoelectric and thermoelectric energy; nevertheless, the systems’ consistency and stability are still not up to mark. Therefore, this paper has depicted a modified new meta-heuristic optimization approach known as the meta-heuristic Harris Hawks Optimization (HHO) algorithm for developing an optimal Proportional Integral (PI) voltage controller to optimize the Hybrid Piezoelectric and Thermoelectric (HPT) energy harvesting system. Besides that, the developed hybrid boost energy harvester interface circuit is evaluated via MATLAB (V2023a) simulation tool. To verify the results, the HHO-based PI controller is compared with the existing Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Lightning Search Algorithm (LSA) based optimization technique. The results showed that the control methods have significance in system stability, robustness, and desired accuracy regarding rising and settling times. The output is regulated at 6.992V DC from an input limit of piezoelectric input of 0.3 V (root mean square) and 0.3V DC thermoelectric input where the achieved voltage gain is 11 times. The results show that the proposed design can scavenge a higher output of 480µW which is a 37% improvement compared to the benchmarking design, and it can extract energy simultaneously from the hybrid source, thereby reducing the circuit complexity and giving the minimum mean absolute error (MAE) to 0.545576.