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Keynote Speakers

Prof. Qutai Sen

Intelligent fault diagnosis of rectifying circuit based on improved particle swarm algorithm

In order to improve the efficiency of intelligent fault diagnosis of rectifying circuit, a method of intelligent fault diagnosis of rectifying circuit based on improved PSO Algorithm is studied. First of all, in order to solve the problem that the convergence precision of elementary particle algorithm (PSO) is low and it is easy to fall into the local minimum, this paper proposes an adaptive strategy to adjust the inertia weight by introducing a disturbance factor, a balanced centralized search and a decentralized and diversified search process. Then, the improved PSO algorithm is used to optimize the weight and threshold of BP neural network, and the neural network is applied to the fault diagnosis of rectifier circuit. The simulation results show that, compared with other methods, the intelligent fault diagnosis method based on improved PSO algorithm has a good fault recognition rate, which provides a theoretical basis for the establishment of automatic fault diagnosis system.

Prof Lixing Zhou

A novel distributed generation schedule of distribution network based on improved bat algorithm

Rational planning of power network distributed generation is a key link to improve energy efficiency, economy, reliability and flexibility of power system. The paper provides a novel distributed generation schedule of Distribution Network based on improved bat algorithms. The goal is to minimize the total investment cost and the power loss of the distributed power system. The static voltage stability index is regarded as a multi-objective programming model of optimization sub-objective. The improved bionic algorithm-bat algorithm is used as an optimization method. Simulation results show the effectiveness of the proposed method.

Prof. Zhixiang Hou
A joint state of charge estimation method for lithium iron phosphate power battery based on GA-AUKF

In this paper, a method based on GA-AUKF for estimating the state of charge of lithium iron phosphate power battery is presented. First, the second order RC equivalent circuit model is established. Then, the forgetting factor recursive least squares is used to identify the parameters of the second order RC equivalent circuit model, and the state equation covariance and measurement equation covariance are calculated by adaptive updating method. Finally, the SOC of power battery is estimated by using forgetting factor recursion least squares and GA-AUKF. The results show that this method can update the state equation covariance and the measurement equation covariance adaptively, and reduce the influence of covariance on the estimation accuracy, and improve the estimation accuracy of SOC.

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