Fuzzy logic control for a wind/battery renewable energy production system

Mengi, Onur Özdal; Altaş, İsmail H.

doi:10.3906/elk-1104-20

Cited by 23 publications

(8 citation statements)

References 28 publications

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“…The wind generator's power output depends on the height of the turbine, the electromechanical conversion efficiency, the wind speed, the air density, and the area swept by the turbine blade [ 24 ].…”

Section: Solar and Wind Resources Data Analysismentioning

confidence: 99%

Fuzzy-based prediction of solar PV and wind power generation for microgrid modeling using particle swarm optimization

Teferra¹,

Ngoo²,

Nyakoe³

2023

Heliyon

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Section: Solar and Wind Resources Data Analysismentioning

confidence: 99%

Fuzzy-based prediction of solar PV and wind power generation for microgrid modeling using particle swarm optimization

Teferra¹,

Ngoo²,

Nyakoe³

2023

Heliyon

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“…The market for photovoltaic (PV) systems is growing at an unprecedented rate. However, an all-solar PV system may run into trouble due to the sun's sporadic output, which depends on things like weather and location [1,3,8,9]. Energy storage devices, such as batteries and ultracapacitors, are often incorporated into the PV system as a solution to this issue [2].…”

Section: Literature Reviewmentioning

confidence: 99%

“…When compared to connecting these locations to the main grid, the cost of constructing a small PV-based system that can power the local demands is far lower. Therefore, the PV-based standalone microgrid, which has gained popularity in recent years [5,8,9,14], is proposed for this purpose. There is potential for lowcost and high-efficiency solar PV panel integration into the microgrid/mini-grid, as well as energy storage devices and regulated loads [9,12,15].…”

Section: Literature Reviewmentioning

confidence: 99%

Design of fuzzy logic controller for solar powered electric vehicle

Bhogireddy,

G V,

et al. 2023

International Conference on Emerging Materials for Technological Applications (ICEMTA 2022)

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In this paper, an EV battery-based energy storage microgrid is controlled by the fuzzy logic controller, which we use as the backbone of an intelligent energy management system (EMS). Plug-in electric vehicles (EVs) can join the microgrid via a DC rapid charging station. Fuzzy logic controllers are used for each EV's estimated departure time, battery charge level, and rated capacity to calculate how much power of each EV can provide toV2G (vehicle-to-grid) and G2V (grid-to-vehicle) services. An array of solar panels and a battery attached to the Microgridthrough a bidirectional dime converter and a boost converter are the core components of the PVDCM. Both positive and negative currents can move from the power source tothe load whether the PV panels are set up for Maximum power point tracking (MPPT) or maximum voltage operation. To maximize efficiency in the faceof high input current and low input voltage, a new design for a solar the DC/DC converter is givenin this paper. However, thehigh DC/DC converter output power means that a big value of the input current is to be expected, which will possess adetrimental impact on the circuit design and the system's stability. In addition to being able to accept a wide variety of input voltage while maintaining a constant output voltage, the DC/DC converter is protecting the battery from being exposed to an output current, and improves the battery's useful life. Inverter current must be tracked and converted from abc to dq0 for this method to work (Clarke and Park transformation). Here the, two orthogonal phase variables for a Fuzzy controller are carried out by the Park transformation (DQ) on a single-phase inverter.

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“…dye-sensitized, perovskite, organic, copper zinc tin sulphide or quantum dot solar cells) is implemented [24,25]. Nowadays, the largest share of PV panels available on the market, mainly due to favourable efficiency to total investment costs ratio and widespread infrastructure, is covered by the crystalline silicon cells (Si-wafer based PV cells represent about 94% of total production worldwide) [2,26]. This situation should change in the future, when cheaper solutions concerning manufacturing highly-efficient PV cells are adopted or non-conventional (i.e.…”

Section: The Possibilities To Use Boost Energy Yield From Pv Systemsmentioning

confidence: 99%

“…where: 𝐻 e,b is the direct (beam) daily radiation intensity, 𝐻 e,d is the diffuse daily radiation intensity, 𝐾 t is the transparency coefficient (daily clearness index) of the atmosphere (0.3-0.6) [26], 𝜔 is the hour angle of the sun over the horizon (index "sr" concerns sunrise, "ss" -sunset), 𝐺 e,0 is the temporary flux density of radiation (solar irradiance) outside the atmosphere, 𝜙 is the latitude of a location, 𝛿 is the declination.…”

Section: Determination Of the Energy Generation Potential In Pv Cellsmentioning

confidence: 99%

Archives of Electrical Engineering

Cholewiński,

Fąfara

2022

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The main objective of this article is to assess the legitimacy of using different tracking systems applied to the photovoltaic panels, for the city of Wroclaw (Poland), using 2 numerical tools: the CM SAF (Climate Monitoring Satellite Application Facility) and PVGIS (Photovoltaic Geographical Information System). In order to identify the solar irradiation, the CM-SAF database (based on the measurements of MFG -Meteosat First Generation -and MSG -Meteosat Second Generation -satellites) was utilised, while the PVGIS (Photovoltaic Geographical Information System) -to calculate the energy yield from PV panels. Particular attention was given to the optimisation of the annual tilt angle and the determination of the energy benefits from the implementation of the various sun tracking systems. Conducted studies showed that up to 30% more electricity yearly can be yielded after the replacement of PV cells with optimally fixed both azimuth and tilt angles by the 2-axis tracking system (179 kWh/m 2 instead of 138 kWh/m 2 ). Moreover, by the adequate decreasing of tilt angles in the summer time or obtaining the most favourable local solar exposure conditions, the supply curve of PV units may be significantly flattened, which may be beneficial when energy storage systems have low capacities.

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Fuzzy logic control for a wind/battery renewable energy production system

Cited by 23 publications

References 28 publications

Fuzzy-based prediction of solar PV and wind power generation for microgrid modeling using particle swarm optimization

Fuzzy-based prediction of solar PV and wind power generation for microgrid modeling using particle swarm optimization

Design of fuzzy logic controller for solar powered electric vehicle

Archives of Electrical Engineering

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