Studying and analyzing the performance of photovoltaic system by using fuzzy logic controller

Salman, Uzba H.; Nawaf, Shahir Fleyeh; Salih, Mohammad Omar

doi:10.11591/eei.v11i3.3680

Cited by 3 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the most successful MPPT optimization strategies is FL. Numerous studies, including [31]- [42], have used FL to make the PV work around the MPP [36]. As shown above, climate is the primary effect on the non-linear features of the I-V and P-V characteristics.…”

Section: Fuzzy Logic Techniquementioning

confidence: 99%

Optimized ANN-fuzzy MPPT controller for a stand-alone PV system under fast-changing atmospheric conditions

2023

View full text Add to dashboard Cite

Solar energy is one of the most promising renewable energy resources. Over the last few decades, photovoltaic (PV) systems have grown in popularity. Since the maximum power point (MPP) of a solar system changes with environmental circumstances, the maximum power point tracking (MPPT) technique is required to get the most power out of the solar system. Various MPPT techniques based on classical and artificial intelligence (AI) methodologies have been proposed in the literature so far. In this paper, we aim to provide a thorough comparative analysis of the most widely used MPPT algorithms based on AI. The MPPT techniques discussed are based on fuzzy logic (FL), artificial neural networks (ANN), and the suggested hybrid approach ANN-fuzzy. The designed MPPT controllers are evaluated in the same PV system, which consists of a PV module, a DC-DC boost converter, and a DC load, under the same weather profile. Using the MATLAB/Simulink simulation tool, the tracking accuracy, response time, overshoot, and steady-state ripple of each method are tested in different weather conditions. The simulation results show that the ANN-fuzzy proposed tactic outperforms both the FL and the ANN MPPT controllers in correctly and successfully tracking the maximum power under diverse atmospheric conditions.

show abstract

Section: Fuzzy Logic Techniquementioning

confidence: 99%

Optimized ANN-fuzzy MPPT controller for a stand-alone PV system under fast-changing atmospheric conditions

2023

View full text Add to dashboard Cite

show abstract

“…Solar tracking is the traditional way that is utilized to get the most out of the energy that is collected [156] [157]. One other approach that may be taken to get maximum power is called MPPT [6]- [8], [11], [12], [15], [19], [27], [28], [35], [41], [42], [48], [51], [52], [57], [62], [64]- [66], [69]- [71], [74], [77], [80], [82], [83], [85], [89], [90], [93], [95], [96], [98], [106], [108], [112], [113], [158]- [245]. The MPP can be detected with the use of an MPPT algorithm that is placed on the microcontroller [246].…”

Section: Maximum Power Point Tracking (Mppt)mentioning

confidence: 99%

“…Numerous methods are utilized on a regular basis to accomplish the task of tracking maximum power point in PV systems [185]. These methods include hill-climbing, perturbation and observation (P&O), look-up table, incremental conductance (Inc-Cond), linearization-based, DC-link capacitor droop control, curve fitting, extremum seeking control, feedback voltage or current, feedback of power variation with voltage, feedback of power variation with current, firefly algorithm, fractional open-circuit voltage (FOCV), fractional short-circuit current (FSCI), artificial neural network (ANN), fuzzy logic, genetic algorithm, particle swarm optimization (PSO), ant colony based optimization, one cycle control (OCC), ripple correlation control (RCC), parasitic capacitance, and slidingmode [6]- [8], [11], [12], [15], [19], [27], [28], [35], [41], [42], [48], [51], [52], [57], [62], [64]- [66], [69]- [71], [74], [77], [80], [82], [83], [85], [89], [90], [93], [95], [96], [98], [106], [108], [112], [113], [158]- [245].…”

Section: Maximum Power Point Tracking (Mppt)mentioning

confidence: 99%

Modernisation of DC-DC converter topologies for solar energy harvesting applications: A review

Sutikno

Purnama

Aprilianto

et al. 2022

IJEECS

View full text Add to dashboard Cite

Solar photovoltaic (PV) power generation has become increasingly important as a renewable source of energy due to the many benefits it offers. These benefits include the ease with which it can be allocated; the absence of noise; the longer life; the absence of pollution; the shorter amount of time required for installation; the high mobility and portability of its parts; and the capability of its output power to meet peak load requirements. DC-DC converters are typically incorporated into solar energy harvesting systems because they allow for the more efficient exploitation of solar cells. One of the difficulties is in the selection of a suitable converter since this has an effect on the operation of the PV system. This study discusses the modernisation of several different DC-DC converter topologies for solar energy harvesting systems. Some of these topologies are boost, buck-boost, single-ended primary-inductance converter (SEPIC), Cuk, and flyback. The topologies have been compared so that detailed information on the complexity of the hardware, the cost of implementation, the efficiency of the energy transfer elements, the tracking efficiency, and the efficiency of the converters can be provided. This paper will be useful as a handy reference in choosing the best converter topology for solar energy harvesting applications.

show abstract

Systematic Literature Review on Fuzzy Hybrid Methods in Photovoltaic Solar Energy: Opportunities, Challenges, and Guidance for Implementation

et al. 2023

View full text Add to dashboard Cite

The application of fuzzy hybrid methods has significantly increased in recent years across various sectors. However, the application of fuzzy hybrid methods for modeling systems or processes, such as fuzzy machine learning, fuzzy simulation, and fuzzy decision-making, has been relatively limited in the energy sector. Moreover, compared to standard methods, the benefits of fuzzy-hybrid methods for capturing complex problems are not adequately explored for the solar energy sector, which is one of the most important renewable energy sources in electric grids. This paper investigates the application of fuzzy hybrid systems in the solar energy sector compared to other sectors through a systematic review of journal articles published from 2012 to 2022. Selection criteria for choosing an appropriate method in each investigated fuzzy hybrid method are also presented and discussed. This study contributes to the existing literature in the solar energy domain by providing a state-of-the-art review of existing fuzzy hybrid techniques to (1) demonstrate their capability for capturing complex problems while overcoming limitations inherent in standard modeling methods, (2) recommend criteria for selecting an appropriate fuzzy hybrid technique for applications in solar energy research, and (3) assess the applicability of fuzzy hybrid techniques for solving practical problems in the solar energy sector.

show abstract

Studying and analyzing the performance of photovoltaic system by using fuzzy logic controller

Cited by 3 publications

References 20 publications

Optimized ANN-fuzzy MPPT controller for a stand-alone PV system under fast-changing atmospheric conditions

Optimized ANN-fuzzy MPPT controller for a stand-alone PV system under fast-changing atmospheric conditions

Modernisation of DC-DC converter topologies for solar energy harvesting applications: A review

Systematic Literature Review on Fuzzy Hybrid Methods in Photovoltaic Solar Energy: Opportunities, Challenges, and Guidance for Implementation

Contact Info

Product

Resources

About