Search

This paper proposes a multi-objective lion swarm optimization based on multi-agent (MOMALSO) for solving the increasingly complex multi-objective optimization problem in engineering practice. First, the Multi-agent system is introduced into the lion swarm optimization (LSO) algorithm. The optimization mechanism of LSO and the information exchange between the agents are integrated to enhance the local search and global search ability of the algorithm, and the self-learning operation can accelerate the approximate Pareto front obtained by the algorithm near to real front. Besides, the external archive is introduced for extending the LSO into a multi-objective algorithm. Finally, the simulations compared with other three algorithms are performed, and the results show that MOMALSO has significant advantages in both convergence and coverage, which verifies the superiority and effectiveness of the algorithm in multi-objective optimization. [ABSTRACT FROM AUTHOR]

Summary Aiming at the problem that distributed secondary control is vulnerable to FDI attacks in DC microgrid, which affects the stability of the system, and the problems of communication redundancy and energy loss in traditional distributed secondary control, an event triggered fixed time distributed secondary control scheme based on high‐order differentiator is proposed. The high‐order differentiator is used to estimate the FDI attack signal of the system. Then, using the estimated attack signal, a fixed time distributed secondary controller based on event triggering is designed to eliminate the impact of the attack signal, and realize the adjustment of bus voltage and accurate power distribution. The strict stability and the absence of Zeno behavior are proved. The simulation results show that the proposed control strategy can significantly reduce the impact of attacks on the system, and the event triggered fixed time control reduces the energy consumption of the system and the update numbers of the controller, making the system have better dynamic performance. [ABSTRACT FROM AUTHOR]

The state of charge (SOC) of lithium batteries is an important parameter of battery management systems. We aim at the problem that the noise variance is fixed during the estimation of the battery state by the unscented Kalman filter (UKF), which leads to low estimation accuracy. Lithium battery SOC estimation based on the UKF and whale optimization algorithm (WOA) is proposed. The first WOA is used to identify the parameters of the battery model. WOA–UKF is used to estimate the SOC of the battery, in which the observed noise variance and process noise variance of the UKF are updated through the second WOA, thereby the estimation accuracy is improved. The experimental results verify the effectiveness of the improved method. [ABSTRACT FROM AUTHOR]