Applying Modified Discrete Particle Swarm Optimization Algorithm and Genetic Algorithm for system identification

Badamchizadeh, Mohammad Ali; Madani, K.

doi:10.1109/iccae.2010.5451412

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…Carefully establishing a sliding surface is a crucial aspect of designing an ASMC system because it distinctly shows the difference between the system's good and bad states. ASMC has been utilized to regulate and manage various types of renewable energy sources, including wind turbines, solar PV systems, and fuel cells [18] and [19].…”

Section: Literature Reviewmentioning

confidence: 99%

Designing of a PSO-Based Adaptive SMC With a Multilevel Inverter for MPPT of PV Systems Under Rapidly Changing Weather Conditions

Anssari,

Badamchizadeh,

Ghaemi

2024

IEEE Access

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This study discusses the widespread problem of chattering phenomena and high frequency oscillations in sliding mode controllers (SMCs) used for maximum power point (MPP) tracking (MPPT) in photovoltaic (PV) systems. Moreover, this study introduces an adaptive sliding mode controller (ASMC) made just for PV panels with MPPT capability to address these problems. The main goal of the control method is to make PV systems more reliable and effective by precisely tracking the MPP, even when the weather quickly changes. This strategy demonstrates superior static and dynamic responsiveness compared with traditional methods. The particle swarm optimization (PSO) algorithm is employed to determine the best gains for the SMC method, specifically for controlling the variable step of the standard perturb and observe (P&O) algorithm. This algorithm used in the study with a Soltech 1STH-250-WH module is linked to a boost DC-DC converter. The aforementioned converter provided power to a resistive load, which was then controlled by a voltage regulator. Subsequently, the research highlights the utilization of a new voltage source inverter: a 9-level diode clamp inverter (DCI) employed to mitigate harmonics, hence enhancing power quality through the reduction of its components and generating a sinusoidal AC voltage that has multiple uses.

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Section: Literature Reviewmentioning

confidence: 99%

Designing of a PSO-Based Adaptive SMC With a Multilevel Inverter for MPPT of PV Systems Under Rapidly Changing Weather Conditions

Anssari,

Badamchizadeh,

Ghaemi

2024

IEEE Access

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“…Some of them are based on populations such as Genetic Algorithms (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO) [11]. They have been widely used in the area of system identification [12][13][14][15] and with the objective of optimizing excitation signals such as multisine [15,16].…”

Section: Introductionmentioning

confidence: 99%

Discrete Interval Binary Signal Optimization Using PSO

Fernandez¹,

Parpinelli²

2018

Proceedings XIII Brazilian Congress on Computational Intelligence

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Discrete Interval Binary Signals (DIBS) is a well-known multifrequency two-level amplitude signal used in system identification and commonly occurs harmonic distortion in the frequency spectrum as a drawback. Hence, an optimization must be done to improve the relation between signal and undesired harmonic distortion to improve the Crest Factor (CR). This paper proposes the use of Particle Swarm Optimization (PSO) to optimize DIBS. The employed PSO is the canonical codified in the continuous domain. Eight different experiments with different parameters of DIBS are proposed to validate this PSO application. PSO performance is compared with the Van Den Bos algorithm widely used in the literature. Results show better performance for PSO compared to the Van Den Bos algorithm passing the Wilcoxon rank sum test with a very small p-value. Another remarkable fact is that PSO shows good convergence to all problem instances, giving robustness to the solution.

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“…While PSO provides efficient and satisfactory solutions like other meta-heuristic methods, it also enables to solve many problems with more accurate results than traditional methods, like Genetic Algorithms (GA) (Badamchizadeh & Madani, 2010;Eberhart & Shi, 1998;Liaoa, Tsengb, & Luarnb, 2007) .…”

Section: Introductionmentioning

confidence: 99%

“…Initially, the PSO was developed to solve optimal problems in a continuous space. It was later adapted to solve problems in a discrete space of solutions (Kennedy & Eberhart, 1997;Al-kazemy & Mohan, 2000;Clerc, 2004;Yin, 2004;Badamchizadeh & Madani, 2010;Ho, Jian, & Lin, 2010;Liaoa, Tsengb, & Luarnb, 2007;Liu, Wang, Ding, & Gao, 2010;Yare & Venayagamoorthy, 2007). Miranda and Fonseca (2002) proposed a combination of the particle swarm optimization and evolutionary principle in which an evolutionary process creates new particles.…”

Section: Introductionmentioning

confidence: 99%

A Swarm Intelligence Method Combined to Evolutionary Game Theory Applied to the Resources Allocation Problem

Leboucher

Chelouah²,

Siarry

et al. 2012

International Journal of Swarm Intelligence Research

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This paper addresses an allocation problem and proposes a solution using a swarm intelligence method. The application of swarm intelligence has to be discrete. This allocation problem can be modelled as a multi-objective optimization problem where the authors minimize the time and the distance of the total travel in a logistic context. This study uses a hybrid Discrete Particle Swarm Optimization (DPSO) method combined to Evolutionary Game Theory (EGT). One of the main implementation issues of DPSO is the choice of inertial, individual, and social coefficients. In order to resolve this problem, those coefficients are optimised by using a dynamical approach based on EGT. The strategies are either to keep going with only inertia, only with individual, or only with social coefficients. Since the optimal strategy is usually a mixture of the three, the fitness of the swarm can be maximized when an optimal rate for each coefficient is obtained. Evolutionary game theory studies the behaviour of large populations of agents who repeatedly engage in strategic interactions. Changes in behaviour in these populations are driven by natural selection via differences in birth and death rates. To test this algorithm, the authors create a problem whose solution is already known. This study checks whether this adapted DPSO method succeeds in providing an optimal solution for general allocation problems.

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Applying Modified Discrete Particle Swarm Optimization Algorithm and Genetic Algorithm for system identification

Cited by 5 publications

References 7 publications

Designing of a PSO-Based Adaptive SMC With a Multilevel Inverter for MPPT of PV Systems Under Rapidly Changing Weather Conditions

Designing of a PSO-Based Adaptive SMC With a Multilevel Inverter for MPPT of PV Systems Under Rapidly Changing Weather Conditions

Discrete Interval Binary Signal Optimization Using PSO

A Swarm Intelligence Method Combined to Evolutionary Game Theory Applied to the Resources Allocation Problem

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