A new implementation of the MPPT based raspberry Pi embedded board for partially shaded photovoltaic system

Fathy, Ahmed; Atitallah, Ahmed Ben; Yousri, Dalia; Rezk, Hegazy; Al‐Dhaifallah, Mujahed

doi:10.1016/j.egyr.2022.04.035

Cited by 16 publications

(3 citation statements)

References 32 publications

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“…One such method is Particle Swarm Optimization (PSO), which simulates the social behavior of birds flocking or fish schooling to iteratively search for optimal solutions. In [19] , the authors proposed a PSO-based MPPT algorithm that dynamically adjusts its parameters to enhance tracking efficiency under varying environmental conditions. While PSO offers advantages such as simplicity and robustness, its performance may be sensitive to parameter settings, requiring careful tuning to achieve optimal results.…”

Section: Literature Reviewmentioning

confidence: 99%

Brain Inspired Intelligent Maximum Power Point Tracking Algorithm for Grid connected Photovoltaic System

Velpula,

Kona

2024

Preprint

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This paper proposes a novel brain inspired intelligent maximum power point tracking (BIIM) algorithm to enhance the performance of grid-connected solar photovoltaic (PV) system. A computational model for the algorithm is developed and implemented by mimicking the functionalities of amygdala, thalamus, orbitofrontal cortex and sensory cortex, present in the limbic system of the human brain. Conventional maximum power point tracking (MPPT) algorithms, such as perturb and observe (P&O) and incremental conductance (IC), often encounter challenges in achieving efficient and accurate maximum power point (MPP) tracking under varying environmental conditions. These algorithms exhibit slow convergence speed, oscillations around the MPP, and reduced tracking accuracy, leading to power losses and suboptimal photovoltaic system performance. To overcome these limitations, a novel brain-inspired intelligent MPPT (BIIM) algorithm has been proposed and implemented. The proposed BIIM algorithm incorporates the limbic system's ability to process sensory inputs, learn from past experiences, and make rapid decisions to achieve efficient MPPT. The efficacy of the proposed BIIM algorithm is comprehensively evaluated under various critical conditions, such as irradiance variations, temperature fluctuations, and grid faults. To validate the effectiveness of the proposed MPPT approach, comparative analysis was conducted with conventional MPPT algorithms such as P&O, proportional-integral (PI) controlled incremental conductance (IC-PI) and particle swarm optimization (PSO). The simulation results reveals the superior dynamic performance of the BIIM algorithm by reducing the response time, undershoot, along with increased efficiency and robustness.

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Section: Literature Reviewmentioning

confidence: 99%

Brain Inspired Intelligent Maximum Power Point Tracking Algorithm for Grid connected Photovoltaic System

Velpula,

Kona

2024

Preprint

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“…But, the size of the L2 cache memory is 1 MB. The Raspberry Pi 4 comes with 4 GB of RAM, which depends on the model, and provides several input/output ports, such as 4 Universal Serial Bus (USB) ports, a High-Definition Multimedia Interface (HDMI) port, a Secure Digital (SD) card, etc., allowing us to develop and implement an embedded system for several applications [20]. The Raspberry Pi 4 is controlled by an OS like Raspberry Pi OS [21], which offers a flexible and efficient environment to develop and evaluate any application through a high-level programming language (e.g., Python, Java, C/C++, etc).…”

Section: Page Layout Analysismentioning

confidence: 99%

Embedded System Based Raspberry Pi 4 for Text Detection and Recognition

Alanazi¹

2023

Intelligent Automation &Amp; Soft Computing

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Detecting and recognizing text from natural scene images presents a challenge because the image quality depends on the conditions in which the image is captured, such as viewing angles, blurring, sensor noise, etc. However, in this paper, a prototype for text detection and recognition from natural scene images is proposed. This prototype is based on the Raspberry Pi 4 and the Universal Serial Bus (USB) camera and embedded our text detection and recognition model, which was developed using the Python language. Our model is based on the deep learning text detector model through the Efficient and Accurate Scene Text Detector (EAST) model for text localization and detection and the Tesseract-OCR, which is used as an Optical Character Recognition (OCR) engine for text recognition. Our prototype is controlled by the Virtual Network Computing (VNC) tool through a computer via a wireless connection. The experiment results show that the recognition rate for the captured image through the camera by our prototype can reach 99.75% with low computational complexity. Furthermore, our prototype is more performant than the Tesseract software in terms of the recognition rate. Besides, it provides the same performance in terms of the recognition rate with a huge decrease in the execution time by an average of 89% compared to the EasyOCR software on the Raspberry Pi 4 board.

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“…Among the most common algorithmic methods are Perturbation and Observation (P&0), hill climbing, INcremental Conductance (INC), short circuit current tracking, open-circuit voltage tracking, and ripple correlation techniques [1][2][3][4][5][6]. Some algorithms have been tailored to reduce hardware requirements and enhance performance [7][8][9][10][11]. Unfortunately, these methods are reliable only when the photovoltaic generator (PV) is exposed to uniform irradiation, where only one peak appears in the photovoltaic supply voltage characteristics.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Genetic and Cuckoo Search Algorithms for MPPT in Partial Shaded

2024

ijSmartGrid

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The exploitation of solar energy through photovoltaic technology has become crucial for electricity production. To maximize this production, optimizing solar radiation is essential. However, various challenges for example fixed structures, structures, and winds carrying sand particles can diminish light intensity, thereby reducing efficiency. These conditions may result in multiple maximum power points appear on the static characteristic of maximum power as a function of photovoltaic voltage. curve. Consequently, more efficient metaheuristic algorithms for maximum power point tracking are needed to ensure optimal operation, particularly during inclement weather and instances of partial shading, such as non-uniform irradiation. This work provides a comparative analysis of metaheuristic approaches specifically Cuckoo Search and Genetic Algorithm, used for tracking the maximum power point in photovoltaic systems under partial shading conditions.

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A new implementation of the MPPT based raspberry Pi embedded board for partially shaded photovoltaic system

Cited by 16 publications

References 32 publications

Brain Inspired Intelligent Maximum Power Point Tracking Algorithm for Grid connected Photovoltaic System

Brain Inspired Intelligent Maximum Power Point Tracking Algorithm for Grid connected Photovoltaic System

Embedded System Based Raspberry Pi 4 for Text Detection and Recognition

Analysis of Genetic and Cuckoo Search Algorithms for MPPT in Partial Shaded

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