Application of mutant particle swarm optimization for MPPT in photovoltaic system

Hoang, Thom Thi; Le, Thi Huong

doi:10.11591/ijeecs.v19.i2.pp600-607

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…By analogy with other work in this context, Azad et al [13] tested the validity of the INC algorithm and found that it performs well under variable weather conditions, but is still slower (on the order of 0.2 second) and has more maximum point author oscillation than the FL algorithm. Hoang and Le [18] also found that the PSO algorithm performs well, but its convergence speed is relatively slow (on the order of 0.87 second). It is clear then that the FL algorithm we have studied and implemented for the power maximization of this installation is more optimal in terms of speed, performance, accuracy and reliability than the traditional P&O algorithm we have treated as a reference, and the INC and PSO algorithms for other works we have cited.…”

Section: Discussionmentioning

confidence: 97%

Study, simulation and realization of a fuzzy logic-based MPPT controller in an isolated DC microgrid

Youssfi,

Alioui,

El Kadi

2024

IJEECS

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This study presents a pioneering methodology for implementing the maximum power point tracking (MPPT) controller, based on fuzzy logic. Through a comprehensive performance analysis, we evaluate its effectiveness compared to the widely used perturb and observe (P&O) algorithm, which is a common MPPT technique. The main objective of our proposed MPPT approach is to improve the performance of a photovoltaic (PV) system. To evaluate the performance of the proposed MPPT controller and compare it with the P&O algorithm, we designed and simulated both controllers using MATLAB/Simulink. We also implemented a prototype of the controllers using an Arduino Mega board, and evaluated their performance under real operating conditions. The experimental results unequivocally confirm that the fuzzy logic-based MPPT controller outperforms the P&O algorithm in terms of performance, speed and accuracy. The fuzzy logic controller offers greater accuracy in tracking the maximum power point under various environmental circumstances, including variations in solar irradiation and connected load. Overall, this work contributes to the development of efficient and reliable MPPT controllers for PV systems, and provides a comparison of the performances of two popular MPPT techniques. Future research could explore other MPPT techniques and evaluate their performance using similar experimental setups.

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Section: Discussionmentioning

confidence: 97%

Study, simulation and realization of a fuzzy logic-based MPPT controller in an isolated DC microgrid

Youssfi,

Alioui,

El Kadi

2024

IJEECS

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“…A PV array is made up of multiple modules that are interconnected in a series-parallel layout, represented as a matrix with NS rows and NP columns, as illustrated in Figure 2 [23]. The PV array is mathematically defined by:…”

Section: Problem Descriptionmentioning

confidence: 99%

Development of deep reinforcement learning for maximum power point tracking of photovoltaic systems

Thom Hoang,

Huong Le

2024

IJEECS

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The use of renewable energy systems, specifically photovoltaic (PV) systems (PVs) that convert solar energy into electricity, has become a popular solution to address global environmental concerns by reducing the utilization of non-renewable energy sources, which contribute to pollution. Efforts to increase the power transfer effectiveness of PV systems include the advancement of controllers for maximizing power point tracking (MPPT). These controllers guarantee optimal system operation at the maximum power point (MPP) in diverse environmental conditions. The paper proposes an improved deep reinforcement learning (DRL) method, namely deep deterministic policy gradient (DDPG), to capture the MPP in PV systems, particularly when dealing with partial shading conditions (PSCs). Unlike reinforcement learning methods that only work with discrete state and action spaces, the proposed DDPG method can handle continuous action state spaces. Feasibility analysis is conducted using MATLAB/Simulink simulations, and the findings demonstrate the efficiency and superior performance of the suggested solutions, highlighting their potential for future use.

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“…This MPPT feature is used to track the maximum power point (MPP), where at this point, the maximum power harvesting process occurs [1]- [3]. The MPP point can be reached if the system operating voltage is set to the MPP voltage [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of evolutionary-based maximum power point tracking for partial shaded photovoltaic

Megantoro

Kusuma

Awalin

et al. 2022

IJECE

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The characteristics of the photovoltaic module are affected by the level of solar irradiation and the ambient temperature. These characteristics are depicted in a V-P curve. In the V-P curve, a line is drawn that shows the response of changes in output power to the level of solar irradiation and the response to changes in voltage to ambient temperature. Under partial shading conditions, photovoltaic (PV) modules experience non-uniform irradiation. This causes the V-P curve to have more than one maximum power point (MPP). The MPP with the highest value is called the global MPP, while the other MPP is the local MPP. The conventional MPP tracking technique cannot overcome this partial shading condition because it will be trapped in the local MPP. This article discusses the MPP tracking technique using an evolutionary algorithm (EA). The EAs analyzed in this article are genetic algorithm (GA), firefly algorithm (FA), and fruit fly optimization (FFO). The performance of MPP tracking is shown by comparing the value of the output power, accuracy, time, and tracking effectiveness. The performance analysis for the partial shading case was carried out on various populations and generations.

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Application of mutant particle swarm optimization for MPPT in photovoltaic system

Cited by 5 publications

References 11 publications

Study, simulation and realization of a fuzzy logic-based MPPT controller in an isolated DC microgrid

Study, simulation and realization of a fuzzy logic-based MPPT controller in an isolated DC microgrid

Development of deep reinforcement learning for maximum power point tracking of photovoltaic systems

Comparative analysis of evolutionary-based maximum power point tracking for partial shaded photovoltaic

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