Optimal PV array reconfiguration under partial shading condition through dynamic leader based collective intelligence

Abstract

This paper applies the innovative idea of DLCI to PV array reconfiguration under various PSCs to capture the maximum output power of a PV generation system. DLCI is a hybrid algorithm that integrates multiple meta-heuristic algorithms. Through the competition and cooperation of the search mechanisms of different metaheuristic algorithms, the local exploration and global development of the algorithm can be effectively improved to avoid power mismatch of the PV system caused by the algorithm falling into a local optimum. A series of discrete operations are performed on DLCI to solve the discrete optimization problem of PV array reconfiguration. Two structures (DLCI-I and DLCI-II) are designed to verify the effect of increasing the number of sub-optimizers on the optimized performance of DLCI by simulation based on 10 cases of PSCs. The simulation shows that the increase of the number of sub-optimizers only gives a relatively small improvement on the DLCI optimization performance. DLCI has a significant effect on the reduction in the number of power peaks caused by PSC. The PV array-based reconstruction system of DLCI-II is reduced by 4.05%, 1.88%, 1.68%, 0.99% and 3.39%, when compared to the secondary optimization algorithms.

Suppression of Railway Catenary Galloping Based on Structural Parameters’ Optimization

“…With the development of artificial intelligence, neural network and deep learning approaches have been widely used in structural response analysis [29][30][31]. Previous studies have demonstrated that neural network-based optimization [32][33][34][35] and control strategies [36,37] have a promising potential to improve performance.…”