A novel unified maximum power extraction framework for PMSG based WECS using chaotic particle swarm optimization derivatives

Dursun, Emre Hasan; Koyuncu, Hasan; Kulaksız, Ahmet Afşin

doi:10.1016/j.jestch.2020.05.005

Cited by 23 publications

(16 citation statements)

References 36 publications

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“…Therefore, it is widely used in practical engineering areas. For example, PSO is usually applied to image processing [ 6 ], parameter optimization [ 7 , 8 ], scheduling optimization [ 9 ], clustering [ 10 ], and price forecasting [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

UCPSO: A Uniform Initialized Particle Swarm Optimization Algorithm with Cosine Inertia Weight

Zhang

Sheng

et al. 2021

Computational Intelligence and Neuroscience

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The particle swarm optimization algorithm (PSO) is a meta-heuristic algorithm with swarm intelligence. It has the advantages of easy implementation, high convergence accuracy, and fast convergence speed. However, PSO suffers from falling into a local optimum or premature convergence, and a better performance of PSO is desired. Some methods adopt improvements in PSO parameters, particle initialization, or topological structure to enhance the global search ability and performance of PSO. These methods contribute to solving the problems above. Inspired by them, this paper proposes a variant of PSO with competitive performance called UCPSO. UCPSO combines three effective improvements: a cosine inertia weight, uniform initialization, and a rank-based strategy. The cosine inertia weight is an inertia weight in the form of a variable-period cosine function. It adopts a multistage strategy to balance exploration and exploitation. Uniform initialization can prevent the aggregation of initial particles. It distributes initial particles uniformly to avoid being trapped in a local optimum. A rank-based strategy is employed to adjust an individual particle’s inertia weight. It enhances the swarm’s capabilities of exploration and exploitation at the same time. Comparative experiments are conducted to validate the effectiveness of the three improvements. Experiments show that the UCPSO improvements can effectively improve global search ability and performance.

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Section: Introductionmentioning

confidence: 99%

UCPSO: A Uniform Initialized Particle Swarm Optimization Algorithm with Cosine Inertia Weight

Zhang

Sheng

et al. 2021

Computational Intelligence and Neuroscience

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“…To extract all available wind power, several researchers have developed different maximum power point tracking (MPPT) techniques for regulating the generated mechanical power from the wind turbine (WT) under changeable climate environments. In recent literature [ 6 , 7 ], the MPPT algorithms are clustered into the direct power controller (DPC) and indirect power controller (IPC), as shown in Figure 1 .…”

Section: Introductionmentioning

confidence: 99%

Robust Adaptive HCS MPPT Algorithm-Based Wind Generation System Using Model Reference Adaptive Control

Alrowaili

Ali

Youssef

et al. 2021

Sensors

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To treat the stochastic wind nature, it is required to attain all available power from the wind energy conversion system (WECS). Therefore, several maximum power point tracking (MPPT) techniques are utilized. Among them, hill-climbing search (HCS) techniques are widely implemented owing to their various features. Regarding current HCS techniques, the rotor speed is mainly perturbed using predefined constants or objective functions, which makes the selection of step sizes a multifaceted task. These limitations are directly reflected in the overall dynamic WECS performance such as tracking speed, power fluctuations, and system efficiency. To deal with the challenges of the existing HCS techniques, this paper proposes a new adaptive HCS (AD-HCS) technique with self-adjustable step size using model reference adaptive control (MRAC) based on the PID controller. Firstly, the mechanical power fluctuations are detected, then the MRAC continuously optimizes the PID gains so as to generate an appropriate dynamic step size until harvesting the maximum power point (MPP) under the optimal tracking conditions. Looking specifically at the simulation results, the proposed AD-HCS technique exhibits low oscillations around the MPP and a small settling time. Moreover, WECS efficiency is increased by 5% and 2% compared to the conventional and recent HCS techniques, respectively. Finally, the studied system is confirmed over a 1.5 MW, gird-tied, double-fed induction generator (DFIG) WECS using MATLAB/Simulink.

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“…In recent years, the world is experiencing a shift towards the adoption of alternative, cheaper and cleaner energy sources [1]. This is driven by the rapid decline of global reserves of conventional energy resources and the potential threats fossil fuels are causing to the environment such as pollution and global warming [2].…”

Section: Introductionmentioning

confidence: 99%

Optimal Fractional-Order PI Control Design for a Variable Speed PMSG-Based Wind Turbine

Noureddine¹,

Morsli²,

Allaoui³

et al. 2021

JESA

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This paper focusses on the design of optimal control strategies for a variable-speed wind energy system based on Permanent Magnet Synchronous Generator (PMSG). The fractional order PI controller, denoted PIλ, is an extension of the classical PI controller, which provides greater flexibility, better performance and robustness, however the tuning of the controller parameters is challenging. In this work, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) provide approximate solutions to various problems and form a good optimization. In our system, they are used to have the PI regulator parameters and tune the parameters of the proposed controllers. The proposed controllers have been applied as maximum power point (MPPT) controllers for the wind turbine and to regulate the PMGS currents under variable weather conditions and. The results show that, among all these controllers, the fractional order PI controller optimized by the PSO leads to better performance in terms of the transient response characteristics such overshoot, rise time and settling time.

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A novel unified maximum power extraction framework for PMSG based WECS using chaotic particle swarm optimization derivatives

Cited by 23 publications

References 36 publications

UCPSO: A Uniform Initialized Particle Swarm Optimization Algorithm with Cosine Inertia Weight

UCPSO: A Uniform Initialized Particle Swarm Optimization Algorithm with Cosine Inertia Weight

Robust Adaptive HCS MPPT Algorithm-Based Wind Generation System Using Model Reference Adaptive Control

Optimal Fractional-Order PI Control Design for a Variable Speed PMSG-Based Wind Turbine

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