A multi-objective pelican optimization algorithm for dynamic reconfiguration of multi-type rural rooftop PV array

Shading and array fault can cause a significant impact on the output power of rural rooftop PV array (RRPVA) and result in power efficiency losses. One of the most popular methods to attenuate the adverse effects of these is reconfiguration in RRPVA. However, the conventional reconfiguration only aims to maximize power output. Hence, this paper proposes a multi-objective pelican optimization algorithm (MOPOA) to improve efficiency and extend the switching life for RRPVA. Comparing the reconfiguration results of the particle swarm algorithm (PSO) and genetic algorithm (GA), the mismatch loss, power loss, performance ratio, and power enhancement percentage of RRPVA under different shading situations are calculated for each of the three algorithms. This paper simulates and analyzes 4×4 symmetric RRPVA and 4×3 asymmetric RRPVA. The results show that MOPOA is 8.4%, 8.5%, 11.2%, 11.5% better than PSO; and 3.8%, 3.5%, 7.6%, 5.6% better than GA in terms of percentage power enhancement (Pen) in 4×4 symmetric RRPVA. In the 4×3 asymmetric RRPVA, the Pen of MOPOA is 5.6%, 9.0%, 10.5%, 9.4% better than PSO, and 4.2%, 2.6%, 3.6%, 2.8% better than GA, respectively. In the case of array fault, the power enhancements were 19.4% and 18.3%, respectively.