scholarly journals Simultaneous Identification of Model Structure and the Associated Parameters for Linear Systems Based on Particle Swarm Optimization

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Semin Chun ◽  
Tae-Hyoung Kim
Keyword(s):  
Particle Swarm Optimization ◽  
Linear Systems ◽  
Heuristic Method ◽  
Particle Swarm ◽  
Model Parameters ◽  
Model Structure ◽  
Optimal Model ◽  
Swarm Optimization ◽  
Optimal Dimension ◽  
Simultaneous Identification

In this study, a novel easy-to-use meta-heuristic method for simultaneous identification of model structure and the associated parameters for linear systems is developed. This is achieved via a constrained multidimensional particle swarm optimization (PSO) mechanism developed by hybridizing two main methodologies: one for negating the limit for fixing the particle’s dimensions within the PSO process and another for enhancing the exploration ability of the particles by adopting a cyclic neighborhood topology of the swarm. This optimizer consecutively searches the dimensional optimum of particles and then the positional optimum in the search space, whose dimension is specified by the explored optimal dimension. The dimensional optimum provides the optimal model structure, while the positional optimum provides the optimal model parameters. Typical numerical examples are considered for evaluation purposes, which clearly demonstrate that the proposed PSO scheme provides novel and powerful impetus with remarkable reliability toward simultaneous identification of model structure and unknown model parameters. Furthermore, identification experiments are conducted on a magnetic levitation system and a robotic manipulator with joint flexibility to demonstrate the effectiveness of the proposed strategy in practical applications.

scholarly journals An All-Region State-of-Charge Estimator Based on Global Particle Swarm Optimization and Improved Extended Kalman Filter for Lithium-Ion Batteries

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 321 ◽  
Author(s):  
Xin Lai ◽  
Wei Yi ◽  
Yuejiu Zheng ◽  
Long Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Kalman Filter ◽  
Lithium Ion Batteries ◽  
Extended Kalman Filter ◽  
Particle Swarm ◽  
Lithium Ion ◽  
State Of Charge ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Soc Estimation

In this paper, a novel model parameter identification method and a state-of-charge (SOC) estimator for lithium-ion batteries (LIBs) are proposed to improve the global accuracy of SOC estimation in the all SOC range (0–100%). Firstly, a subregion optimization method based on particle swarm optimization is developed to find the optimal model parameters of LIBs in each subregion, and the optimal number of subregions is investigated from the perspective of accuracy and computation time. Then, to solve the problem of a low accuracy of SOC estimation caused by large model error in the low SOC range, an improved extended Kalman filter (IEKF) algorithm with variable noise covariance is proposed. Finally, the effectiveness of the proposed methods are verified by experiments on two kinds of batteries under three working cycles, and case studies show that the proposed IEKF has better accuracy and robustness than the traditional extended Kalman filter (EKF) in the all SOC range.

scholarly journals Short-term power load forecasting based on support vector machine and particle swarm optimization

2018 ◽  
Vol 13 ◽  
pp. 174830181879706 ◽  
Author(s):  
Song Qiang ◽  
Yang Pu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Influencing Factors ◽  
Load Forecasting ◽  
Particle Swarm ◽  
Forecast Accuracy ◽  
Support Vector ◽  
Model Parameters ◽  
Short Term ◽  
Swarm Optimization

In this work, we summarized the characteristics and influencing factors of load forecasting based on its application status. The common methods of the short-term load forecasting were analyzed to derive their advantages and disadvantages. According to the historical load and meteorological data in a certain region of Taizhou, Zhejiang Province, a least squares support vector machine model was used to discuss the influencing factors of forecasting. The regularity of the load change was concluded to correct the “abnormal data” in the historical load data, thus normalizing the relevant factors in load forecasting. The two parameters are as follows Gauss kernel function and Eigen parameter C in LSSVM had a significant impact on the model, which was still solved by empirical methods. Therefore, the particle swarm optimization was used to optimize the model parameters. Taking the error of test set as the basis of judgment, the optimization of model parameters was achieved to improve forecast accuracy. The practical examples showed that the method in the work had good convergence, forecast accuracy, and training speed.

Power Distribution Network Reconfiguration by Using EPSO for Loss Minimizing

2014 ◽  
Vol 699 ◽  
pp. 809-815 ◽  
Author(s):  
Mohamad Fani Sulaima ◽  
Mohd Hafiz Jali ◽  
Wan Mohd Bukhari ◽  
M.N.M. Nasir ◽  
Hazriq Izzuan Jaafar
Keyword(s):  
Particle Swarm Optimization ◽  
Power Distribution ◽  
Distribution Network ◽  
Heuristic Method ◽  
Particle Swarm ◽  
Computational Time ◽  
Network Reconfiguration ◽  
Voltage Profile ◽  
Power Distribution Network ◽  
Swarm Optimization

Due to the complexity of modern power distribution network, a hybridization of heuristic method which is called as Evolutionary Particle Swarm Optimization (EPSO) is introduced to identify the open and closed switching operation plans for network reconfiguration. The objectives of this work are to reduce the power losses and improve the voltage profile in the overall system meanwhile minimizing the computational time. The proposed combination of Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) is introduced to make it faster in order to find the optimal solution. The proposed method is applied and it impacts to the network reconfiguration for real power loss and voltage profiles is investigated respectively. The proposed method is tested on a IEEE 33-bus system and it is compared to the traditional PSO and EP method accordingly. The results of this study is hoped to help the power engineer to configure the smart and less lossed network in the future.

scholarly journals Comparison of Particle Swarm Optimization and Ziegler Nichols Techniques

2020 ◽  
pp. 184-188
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Optimization Technique ◽  
Heuristic Method ◽  
Particle Swarm ◽  
Absolute Error ◽  
Pso Algorithm ◽  
Computational Method ◽  
Swarm Optimization ◽  
Tuning Method

Two tuning techniques namely: Particle Swarm Optimization (PSO) and Ziegler Nichols (ZN) technique are compared. PSO is an optimization technique based on the movement and intelligence of swarms. PSO applies the concept of social interaction to problem solving. It is a computational method that optimizes a problem by iteratively trying to improve a candidate solution about a given measure of quality. Ziegler Nichols tuning method is a heuristic method of tuning a PID controller. The ZN close loop tuning is performed by setting the I (integral) and D (derivative) gains to zero and increasing proportional gain to obtain sustained oscillations. The DC Motor is represented by second order transfer function is used as a plant, which is controlled using PID controller. The PID controller parameters are chosen by tuning the controller using PSO algorithm and ZN method. The response of the system to unit step input is plotted and performance measures are evaluated for comparing PSO algorithm and ZN technique. Here we have compared the two tuning methods based upon the settling time (Ts), peak overshoot (Mp) and the two performance indices namely Integral square error (ISE) and Integral Absolute error (IAE).

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