Improvement of Conventional MPPT Techniques P&amp;O and INC by Integration of Fuzzy Logic

Zerouali, Mohammed; Zouirech, Salaheddine; Ougli, Abdelghani El; Tidhaf, Belkassem; Zrouri, Hafida

doi:10.1109/irsec48032.2019.9078330

Cited by 13 publications

(6 citation statements)

References 7 publications

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“…Due to the lack of power in communities, farmers who wish to irrigate crops on farms that are stocked with produce must get up in the middle of the night [29]. Additionally, they were unable to touch farmhouses during COVID-19 and water plants, which caused extensive damage to the agricultural sector and a shortage of food [30].…”

Section: Figure 1 Categorize Of Irrigation Systemmentioning

confidence: 99%

Fuzzy rules based smart irrigation system using adaptive bacterial foraging optimization

Bhoyar,

Kanojia,

Chourasia

2024

IOP Conf. Ser.: Earth Environ. Sci.

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The practice of agriculture is heavily reliant on the implementation of irrigation systems. Technology improvements have made it unnecessary to rely on someone else to perform irrigation when we are out and whenever crops need to be watered. Many researchers have attempted to autonomously irrigate crops, but difficulties with accuracy, timing, and cost are rarely addressed and given top priority. The proposed approach employs a real-time sensor, wireless sensor network, the adaptive bacterial foraging optimization (ABFO) algorithm, and a fuzzy irrigation system control to achieve autonomous watering, thereby enabling smart irrigation. This method reduces waste while preserving the container’s water supply. Automated irrigation determines whether crops need to be watered by considering the type of crop, the weather, and the soil moisture and not soil moisture alone. The need for water is calculated using the three aforementioned criteria and fuzzy control drives the automation. Using an arduino-based IoT circuitry, the bioinspired model with algorithm adaptive bacterial foraging optimization, generates the optimized values for three parameters, which are then used by fuzzy logic control to predict the watering requirements of the plants. In terms of accuracy, timeliness, and cost, the suggested approach is advantageous. With this model, it is now possible to completely automate the irrigation system.

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Section: Figure 1 Categorize Of Irrigation Systemmentioning

confidence: 99%

Fuzzy rules based smart irrigation system using adaptive bacterial foraging optimization

Bhoyar,

Kanojia,

Chourasia

2024

IOP Conf. Ser.: Earth Environ. Sci.

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“…Extensively used and practical, P&O offers stable shadowing and bettered energy effectiveness. [8][9].…”

Section: Perturbation and Observation (Pando)mentioning

confidence: 99%

A Comparative Study on Photovoltaic using Fuzzy Logic and perturbation and observation

Ballouti,

Chouiekh,

Zakriti

et al. 2023

E3S Web of Conf.

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The global shift to renewable energy, especially solar energy, is critical to reducing our dependence on fossil fuels and solving environmental problems. This study directly compares two methods for solar optimization in photovoltaic systems: perturbation and observation (P&O) and fuzzy logic controller (FLC). We evaluated their performance in different lighting conditions, with the FLC offering stability but slightly slower response times, while the P&O offering real-world reliability, albeit with minimal fluctuations. Our results highlight the significant impact of light intensity on power output. We recommend using P&O for quick tuning and FLC for consistent performance. These insights help decision-makers select the most appropriate methods to maximize photovoltaic efficiency, thereby contributing to sustainable energy solutions.

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“…However, the performances achieved in [7] put this technique aside. In order to remedy this situation, our idea is to replace the fixed step by a floating step taking into account the fuzzy approach.…”

Section: Introductionmentioning

confidence: 99%

A high degree of direct torque control applied to a grid-connected wind energy system based on a DFIG

2022

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This paper presents the performances improvement of a doubly fed induction generator (DFIG) driven by a wind turbine (WT) using direct torque control (DTC). However, the major drawbacks related to DTC are high torque/flux ripples that produce mechanical vibration and disagreeable noise. The use of multilevel inverters seems to be an interesting solution. A three-level voltage source (inverter) converter (3LVSI) connected to the rotor side of the DFIG is considered in this paper. The high freedom degree of the voltage vectors selection in the 3LVSI allows a control with minimal torque and flux ripples. In addition, a fuzzy logic approach is introduced, to ensure an intelligent extraction of the energy sweeping the WT blades. A variable adjustment step enables an optimal extraction in a minimum tracking time with significant reduction of oscillations in the steady state. Simulation results obtained using MATLAB/SIMULINK demonstrate the effectiveness of the 3LVSI-DTC control based on Fuzzy MPPT in the wind energy conversion system (WECS).

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Improvement of Conventional MPPT Techniques P&O and INC by Integration of Fuzzy Logic

Cited by 13 publications

References 7 publications

Fuzzy rules based smart irrigation system using adaptive bacterial foraging optimization

Fuzzy rules based smart irrigation system using adaptive bacterial foraging optimization

A Comparative Study on Photovoltaic using Fuzzy Logic and perturbation and observation

A high degree of direct torque control applied to a grid-connected wind energy system based on a DFIG

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