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Proportional-Integral-Derivative (PID) controllers are widely used in various control systems to enhance stability and performance. In a thermostat, it adjusts heating or cooling based on the temperature difference between the actual and desired levels. They are often used in automotive speed systems, effectively managing sudden speed changes while maintaining a constant speed under varying conditions. On the other hand, PI controllers, commonly employed in voltage regulation, enhance stability...
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Recursive bit assignment with neural reference adaptive step (RNA) MPPT algorithm for photovoltaic system.

Eman Hegazy1, Mona Shokair2, Waleed Saad2,3

  • 1Department of Electrical and Electronic Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32951, Egypt. eng.ehegazy2009@gmail.com.

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This summary is machine-generated.

A novel RNA algorithm enhances photovoltaic (PV) system efficiency by optimizing power output. This Maximum Power Point Tracking (MPPT) method ensures faster tracking and higher energy yield under various conditions.

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Area of Science:

  • Renewable Energy Systems
  • Electrical Engineering
  • Artificial Intelligence in Power Electronics

Background:

  • Photovoltaic (PV) systems are crucial for renewable energy generation.
  • Maximum Power Point Tracking (MPPT) algorithms are essential for maximizing PV energy output.
  • Existing MPPT algorithms face challenges in efficiency and tracking accuracy under dynamic conditions.

Purpose of the Study:

  • To introduce and evaluate a novel RNA algorithm for efficient Maximum Power Point Tracking (MPPT) in photovoltaic systems.
  • To enhance the energy efficiency and tracking speed of PV systems.
  • To validate the proposed algorithm's performance against established MPPT methods.

Main Methods:

  • Development of a two-segment RNA algorithm comprising an artificial neural network for reference power generation and a Recursive Bit Assignment (RBA) network for variable duty cycle control.
  • Simulation of the PV system using MATLAB under varying irradiance and temperature conditions.
  • Comparative analysis of the proposed RNA algorithm with Perturb and Observe, Neural Network, and Adaptive Neural Inference System algorithms.

Main Results:

  • The proposed RNA algorithm demonstrated fast tracking time, high energy efficiency, and accurate tracking of the true maximum power point.
  • The algorithm maintained acceptable ripple levels in the PV system output.
  • Superior performance of the RNA algorithm was observed across various scenarios, including irradiance variations, temperature fluctuations, and partial shading.

Conclusions:

  • The RNA algorithm presents an efficient and robust solution for MPPT in photovoltaic systems.
  • The proposed algorithm significantly improves PV system performance compared to existing methods, especially under challenging environmental conditions.
  • The study validates the effectiveness of the RNA algorithm for practical PV system applications.