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• A novel dynamic unitary PV array reconfiguration method is proposed. • IPDO achieves the highest power over other eight comparison algorithms. • Four metrics are used to quantitatively assess various algorithms. • HIL experiments effectively validate the implementation feasibility of IPDO. • A new switch matrix is designed to reduce the switch number. This work designs an advanced dynamic unitary PV array reconfiguration scheme based on a novel improved prairie dog optimization (IPDO) to solve the power loss issue due to partial shading of PV arrays. This method takes the PV cell as the smallest entity of reconfiguration, and is suitable for large-scale PV arrays. To avoid invalid and redundant relocation of PV modules, an innovative self-rectify strategy is employed and embedded in the reconfiguration algorithm, which can reduce invalid actions of switching matrix by up to 80 %. Comparative simulation tests among nine algorithms (IPDO, genetic algorithm, particle swarm optimization, simulated annealing, four-square sudoku, arithmetic sequence pattern, modified harris hawks optimizer, artificial ecosystem-based optimization, Runge Kutta optimizer) under eight typical shading types verify the superiority of IPDO from four metrics, i.e., power loss, power enhancement, fill factor and execution ratio. Particularly, IPDO reduces power loss by 7.33 % to 30.63 %, enhances the harvested power by 6.1 % to 31.59 %, enhances fill factor (FF) by 0.579 to 0.773, and enhances execution ratio (ER) by 60.37 % to 92.67 % for asymmetric (20 × 30) PV array. For symmetric (30 × 30) PV array, IPDO reduces power loss by 9.01 % to 27.86 %, enhances the harvested power by 8.33 % to 43.69 %, enhances FF by 0.602 to 0.759, and enhances ER by 72.14 % to 90.99 %. Moreover, a new switching matrix with double-pole multi-throw switches is designed for the required switches number reduction. Lastly, hardware-in-the-loop (HIL) experiments based on RTLAB are carried out which validate the implementation feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

• A new optimal mileage-based PV array reconfiguration (OMAR) is constructed. • The OMAR can maximize the total benefit instead of only the generation benefit. • The OMAR decomposition with two sub-problems reduces the optimization difficulty. • The swarm reinforcement learning is used to obtain high-quality optimums of OMAR. • The proposed method can obtain higher total benefit than 6 comparative algorithms. This paper constructs a new optimal mileage-based PV array reconfiguration (OMAR) in a PV power plant under partial shading conditions. It aims to maximize the power output of a PV power plant, and minimize the additional capacity and mileage payments resulting from the power fluctuation in a performance-based frequency regulation market. To reduce the optimization difficulty of OMAR, it is decomposed into two optimization sub-problems, including an upper-layer discrete optimization of PV array reconfiguration and a lower-layer continuous optimization of real-time generation scheduling. The upper-layer discrete optimization is addressed by the proposed swarm reinforcement learning (SRL), which can implement an efficient exploration and exploitation with multiple cooperative agents instead of a single learning agent. The rest lower-layer optimization is handled by the fast interior point method. The proposed method's effectiveness is thoroughly evaluated on the 10 × 10 total-cross-tied PV arrays under various partial shading conditions. Simulation results demonstrate that the proposed SRL can obtain a larger total benefit than genetic algorithm (GA), particle swarm optimization (PSO), grasshopper optimization algorithm (GOA), harris hawks optimizer (HHO), butterfly optimization algorithm (BOA), and Q-learning, in which the benefit increment can reach from 2.12% (against PSO) to 10.62% (against Q-learning). [ABSTRACT FROM AUTHOR]

• IIGA is applied to TEG system reconfiguration for the first time. • Performance of IIGA is proved via comparison with four meta-heuristic algorithms. • Feasibility of the proposed method is investigated based on a 9 × 9 small TEG system and a 15 × 15 large TEG system. • A HIL experiment is undertaken to verify the reconfiguration performance of IIGA. To deal with the problem of low thermoelectric conversion efficiency in thermoelectric power generation (TEG), this work designs an improved immune genetic algorithm (IIGA) to reconfigure TEG system under non-uniform temperature distribution (NTD) conditions to maximize power output. The traditional IGA is a fusion algorithm that combines genetic algorithm (GA) with immune algorithm (IA). Due to IGA's flaws of strict parameter setting and long computation time, three mountain factors are updated dynamically in IIGA to enhance the balance between local exploitation and global exploration. Consequently, the electrical connection of the TEG system is dynamically adjusted and reconfigured via IIGA, which can effectively improve power generation efficiency and reduce power consumption. The effectiveness and reliability of IIGA are testified and compared against four well-established meta-heuristic algorithms under two cases, i.e., 9 × 9 small TEG system and 15 × 15 large TEG system. Simulation results indicate that the output power boost by IIGA reaches 7.502 % under the 9 × 9 small TEG system and 10.281 % under the 15 × 15 large TEG system. Furthermore, a hardware-in-the-loop (HIL) experiment based on RTLAB platform is undertaken to testify the hardware implementation feasibility of IIGA affected by NTD conditions. [ABSTRACT FROM AUTHOR]