scholarly journals Parameter Identification of Synchronous Generator Based on Improved Genetic Algorithm

Smart Grid ◽  
2018 ◽  
Vol 08 (02) ◽  
pp. 135-142
Author(s):  
文豪 朱
Keyword(s):  
Genetic Algorithm ◽  
Parameter Identification ◽  
Synchronous Generator ◽  
Improved Genetic Algorithm

scholarly journals Unscented Particle Filter for SOC Estimation Algorithm Based on a Dynamic Parameter Identification

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Fang Liu ◽  
Jie Ma ◽  
Weixing Su
Keyword(s):  
Genetic Algorithm ◽  
Parameter Identification ◽  
Particle Filter ◽  
Electric Vehicles ◽  
Estimation Method ◽  
Operating Conditions ◽  
Model Parameters ◽  
Improved Genetic Algorithm ◽  
Soc Estimation ◽  
Unscented Particle Filter

In order to solve the problem that the model-based State of Charge (SOC) estimation method is too dependent on the model parameters in the SOC estimation of electric vehicles, an improved genetic algorithm is proposed in this paper. The method has the advantages of being able to quickly determine the search range, reducing the probability of falling into local optimum, and having high recognition accuracy. Then we can realize online dynamic identification of power battery model parameters and improve the accuracy of model parameter identification. In addition, considering the complex application environment and operating conditions of electric vehicles, an SOC estimation method based on improved genetic algorithm and unscented particle filter (improved GA-UPF) is proposed. And we compare the improved GA-UPF algorithm with the least square unscented particle filter (LS-UPF) and improved GA unscented Kalman filter (improved GA-UKF) algorithm. The comparison results show that the improved GA-UPF algorithm proposed in this paper has higher estimation accuracy and better stability. It also reflects the practicability and accuracy of the improved GA parameter identification algorithm proposed in this paper.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

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