Dynamic and Adaptive Maximum Power Point Tracking Using Sequential Monte Carlo Algorithm for Photovoltaic System

Odat, Alhaj-Saleh; Alzoubi, Omar; Shboul, Bashar; Li, Jia

doi:10.1007/s13369-023-08023-0

Cited by 3 publications

(4 citation statements)

References 17 publications

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“…Furthermore, the system's effectiveness was confirmed through validation both under standard test conditions (STC) and varying weather conditions of irradiance, temperature, and partial shading (simple, moderate, severe, and dynamic shadings). The results demonstrated a remarkable performance and reliability with the proposed approach [1] . Solar Radiation, Temperature, and Partial Shadings Profiles used to evaluate the SMC-MPPT Algorithms …”

Section: Data Descriptionmentioning

confidence: 71%

“…Essential to the simulation design was the seamless integration of the Sequential Monte Carlo (SMC) algorithm [1] . This adaptive algorithm was incorporated into the PV system model to facilitate dynamic MPPT.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…Fig. 1 illustrates the standard test condition (STC) input profile scenario used as input for the Photovoltaic system reference in [1] . In this figure, the input irradiance remains constant at 1000 W/m², and the temperature is maintained at a constant 25 °C, with no partial shading affecting the system.…”

Section: Data Descriptionmentioning

confidence: 99%

“…The mathematical design and methodology of the complete PV system were detailed in our prior research, titled "Dynamic and Adaptive Maximum Power Point Tracking Using Sequential Monte Carlo Algorithm for Photovoltaic System" by Odat et al. (2023) [1] . The provided data facilitate precise replication of the output, saving significant simulation time.…”

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confidence: 99%

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Data of simulation model for photovoltaic system's maximum power point tracking using sequential Monte Carlo algorithm

Odat,

Shawaqfah,

Al-Momani

et al. 2024

Data in Brief

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Section: Data Descriptionmentioning

confidence: 71%

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Section: Data Descriptionmentioning

confidence: 99%

mentioning

confidence: 99%

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Data of simulation model for photovoltaic system's maximum power point tracking using sequential Monte Carlo algorithm

Odat,

Shawaqfah,

Al-Momani

et al. 2024

Data in Brief

Self Cite

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Experimental Analysis of Weather Condition Effects on Photovoltaic Systems’ Performance: A Jordan Case Study

Al-Smairan,

Odat,

Olimat

et al. 2024

Journal of Engineering

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Energy generation and economic development are closely linked, with energy playing a pivotal role in wealth creation. However, the finite nature of fossil fuel resources and associated environmental challenges has emphasized the need for sustainable energy alternatives based on renewable sources. Among these alternatives, photovoltaic (PV) systems hold significant potential for fostering a sustainable energy system. Despite ongoing material research, achieving a major breakthrough in enhancing the conversion efficiency of commercial PV modules remains a challenge. To optimize PV system yield, installation must consider geographic location and design factors. PV modules often yield different results compared to manufacturer specifications due to factors such as high temperatures, dust, and arid to semiarid climates. Therefore, it is crucial to conduct outdoor testing and characterization of PV modules tailored to specific locations, such as Jordan, to maximize system performance. This research aims to examine the impact of ambient temperature, dust accumulation, and solar irradiance intensity on PV system performance in Jordan, providing valuable insights through monitoring power output variations. The findings will contribute to improving PV system efficiency in Jordan’s unique climate and aid manufacturers in developing innovative PV applications. The collected data includes solar radiation, temperature, voltage, current, and output power. The results confirm that the Azraq site is a better location when compared to Mafraq based on the possibility of generated more output power using a PV system.

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Highly Efficient DC–DC Boost Converter Achieved With Improved MPPT‐Based Raspberry Pi for Enhancing Photovoltaic System Generation

Alghaythi,

Fathy,

Allehyani

et al. 2024

International Journal of Energy Research

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The operation of photovoltaic (PV) panels in partial shade (PS) conditions (PSCs) lowers the quantity of energy generated due to the hot spots that increase the power loss. In this scenario, a maximum power point tracker (MPPT) is necessary to monitor the power–voltage curve, which features multiple local peaks in addition to one global peak (GP). The GP could not be extracted from the panel at PSCs using traditional MPPT techniques such as hill climbing (HC), perturb and observe (P&O), and incremental conductance (IC). Therefore, this paper proposes a new construction of MPPT simulated via a Raspberry Pi 4 controller programed through a recent bald eagle search (BES) algorithm. Because of its substantially enhanced diversification, which aids in avoiding local peaks, the suggested BES can capture the global power of PV during PSCs. The Raspberry Pi 4‐BES MPPT in the recommended hardware adjusts the direct current (DC)–DC boost converter’s duty cycle based on its inputs, which are the voltage and current from the PV panel output. Eight shade patterns are studied, and the fetched results through the proposed BES are compared to other programed approaches of skill optimization algorithm (SOA), gray wolf optimizer (GWO), and particle swarm optimization (PSO). Additionally, the proposed tracker’s Simulink model is created and evaluated in relation to the experimental hardware. Moreover, the power errors and efficiencies of all considered approaches are calculated in both simulation and experimental analyses. The results revealed that the least errors are 0.0018% and 0.296% obtained through the proposed Raspberry Pi 4‐BES MPPT in simulation and experimental, respectively. Also, in the simulation and experimental setting, the recommended tracker yielded the highest efficiencies of 99.99% and 99.92%, respectively. The suggested Raspberry Pi 4‐BES MPPT is praised for being a useful tracker for monitoring the GP of PV panels at various PSCs.

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Dynamic and Adaptive Maximum Power Point Tracking Using Sequential Monte Carlo Algorithm for Photovoltaic System

Cited by 3 publications

References 17 publications

Data of simulation model for photovoltaic system's maximum power point tracking using sequential Monte Carlo algorithm

Data of simulation model for photovoltaic system's maximum power point tracking using sequential Monte Carlo algorithm

Experimental Analysis of Weather Condition Effects on Photovoltaic Systems’ Performance: A Jordan Case Study

Highly Efficient DC–DC Boost Converter Achieved With Improved MPPT‐Based Raspberry Pi for Enhancing Photovoltaic System Generation

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