A multi-strategy improved particle swarm optimization algorithm and its application to identifying uncorrelated multi-source load in the frequency domain

Gou, Jianping; Guo, Wang-Ping; Wang, Cheng; Luo, Wei

doi:10.1007/s00521-015-2134-2

Cited by 15 publications

(5 citation statements)

References 67 publications

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“…where i = 1, 2, … , n; n stands for the total number of particles in the population; V i stands for the moving rate; w p stands for the inertia weight whose range of value usually is 0.4~1.2; Pos i stands for the position of the particle; pbest is the location of the best solution in iteration; gbest stands for the location of global best solution; rand stands for a random number between 0 and 1; and c 1 and c 2 stand for the acceleration constants. The values of c 1 and c 2 are 2, which are determined on the basis of the existing research [37]. The position and velocity of a particle in an n-dimensional space can be expressed as…”

Section: Particle Swarm Optimization Algorithm (Pso)mentioning

confidence: 99%

Parameter Optimization on FNN/PID Compound Controller for a Three-Axis Inertially Stabilized Platform for Aerial Remote Sensing Applications

et al. 2019

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This paper presents a composite parameter optimization method based on the chaos particle swarm optimization and the back propagation algorithms for a fuzzy neural network/proportion integration differentiation compound controller, which is applied for an aerial inertially stabilized platform for aerial remote sensing applications. Firstly, a compound controller combining both the adaptive fuzzy neural network and traditional PID control methods is developed to deal with the contradiction between the control precision and robustness due to disturbances. Then, on the basis of both the chaos particle swarm optimization and the back propagation compound algorithms, the parameters of the fuzzy neural network/PID compound controller are optimized offline and fine-tuned online, respectively. In this way, the compound controller can achieve good adaptive convergence so as to get high stabilization precision under the multisource dynamic disturbance environment. To verify the method, the simulations are carried out. The results show that the composite parameter optimization method can effectively enhance the convergence of the controller, by which the stabilization precision and disturbance rejection capability of the proposed fuzzy neural network/PID compound controller are improved obviously.

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Section: Particle Swarm Optimization Algorithm (Pso)mentioning

confidence: 99%

Parameter Optimization on FNN/PID Compound Controller for a Three-Axis Inertially Stabilized Platform for Aerial Remote Sensing Applications

et al. 2019

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“…Optimization problem refers to making certain performance indexes reach the maximum or minimum through appropriate program design and parameter selection under the constraints [1][2][3]. A Professor at Harvard University and a member of the American Academy of Engineering pointed out that "any control and decision-making problem can be essentially reduced to an optimization problem" [4].…”

Section: Introductionmentioning

confidence: 99%

Research on Improved Particle Swarm Computational Intelligence Algorithm and Its Application to Multi-Objective Optimisation

Chen,

Xiong

2024

Applied Mathematics and Nonlinear Sciences

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Due to the pervasive generalization challenges in optimization technology, there is a noticeable trend toward planning and diversifying optimization techniques. This paper focuses on particle swarm optimization algorithms, particularly their application in multi-objective optimization scenarios. Initially, the study examines basic particle swarm, standard particle swarm, and particle swarm algorithms with a shrinkage factor. Subsequently, an enhanced particle swarm optimization algorithm is proposed, incorporating a hybridization model and a convergence factor model tailored to the specific characteristics of particle swarm algorithms. This improved algorithm is then applied to multi-objective optimization problems, establishing a novel algorithm based on the fusion of the enhanced particle swarm approach with constrained optimization. Simulation experiments conducted on this model reveal significant findings. In low-dimensional settings, the algorithm achieves a 100% optimization success rate, marking an average improvement of 53.80%, 40.78%, and 24.76% over competing algorithms. Moreover, in multi-objective optimization simulation experiments, this algorithm generates 142 and 135 optimal solutions, outperforming traditional algorithms by 112 and 107 solutions, respectively. These results validate the efficiency and enhanced performance of the improved particle swarm-based multi-objective optimization algorithm, demonstrating its potential as an effective tool for addressing real-world optimization challenges.

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“…The power system with stressed transmission links provides continuous frequency oscillations with minor variation in the supply. Due to the sudden changes in the load demand and frequency changes, the frequency relays get broken and so the accuracy of the power system gets affected (Gou et al , 2016; Vijaya Chandrakala et al , 2013; Wen, 2011). In power system, the particular area gets blackout due to the abrupt changes in system frequency and voltage stability (Ponnusamy et al , 2015; Elsisi et al , 2016).…”

Section: Introductionmentioning

confidence: 99%

LFOPI controller: a fractional order PI controller based load frequency control in two area multi-source interconnected power system

Sharma

Yadav

2020

DTA

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PurposeIn computer application scenario, data mining task is rarely utilized in power system, as an enhanced part, this work presented data mining task in power systems, to overcome frequency deviation issues. Load frequency control (LFC) is a primary challenging problem in an interconnected multi-area power system.Design/methodology/approachThis paper adopts lion algorithm (LA) for the LFC of two area multi-source interconnected power systems. The LA calculates the optimal gains of the fractional order PI (FOPI) controller and hence the proposed LA-based FOPI controller (LFOPI) is developed.FindingsFor the performance analysis, the proposed algorithm compared with various algorithm is given as, 80.6% lesser than the FOPI algorithm, 2.5% lesser than the GWO algorithm, 2.5% lesser than the HSA algorithm, 4.7% lesser than the BBO algorithm, 1.6% lesser than PSO algorithm and 80.6% lesser than the GA algorithm.Originality/valueThe LFOPI controller is the proposed controlling method, which is nothing but the FOPI controller that gets the optimal gain using the LA. This method produces better performance in terms of converging behavior, optimization of controller gain, transient profile and steady-state response.

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A multi-strategy improved particle swarm optimization algorithm and its application to identifying uncorrelated multi-source load in the frequency domain

Cited by 15 publications

References 67 publications

Parameter Optimization on FNN/PID Compound Controller for a Three-Axis Inertially Stabilized Platform for Aerial Remote Sensing Applications

Parameter Optimization on FNN/PID Compound Controller for a Three-Axis Inertially Stabilized Platform for Aerial Remote Sensing Applications

Research on Improved Particle Swarm Computational Intelligence Algorithm and Its Application to Multi-Objective Optimisation

LFOPI controller: a fractional order PI controller based load frequency control in two area multi-source interconnected power system

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