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The power transmission to a factory involves the transfer of apparent power, a combination of active and reactive power. The power factor measures how effectively electrical power is converted into useful work output. The ratio of the real power (KW) that does the work to the apparent power (KVA) supplied to the circuit.
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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
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Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
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Updated: Jun 22, 2025

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Solar Tracking Control Algorithm Based on Artificial Intelligence Applied to Large-Scale Bifacial Photovoltaic Power

José Vinícius Santos de Araújo1, Micael Praxedes de Lucena2, Ademar Virgolino da Silva Netto1

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This study introduces an AI algorithm for solar trackers, optimizing energy generation by considering factors like weather and panel distance. The new system demonstrated significant energy gains compared to traditional trackers.

Keywords:
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Area of Science:

  • Renewable Energy
  • Artificial Intelligence
  • Solar Power Optimization

Background:

  • The global shift to a low-carbon economy necessitates advancements in solar energy technologies.
  • Solar trackers enhance photovoltaic (PV) plant capacity by following the sun's path.
  • Optimizing solar tracker performance requires accounting for variables like panel spacing, reflectivity, bifacial panels, and climate fluctuations.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI)-based algorithm for solar trackers.
  • To integrate key environmental and operational factors, including weather variations and inter-panel distance, into solar tracking.
  • To improve the energy generation efficiency of photovoltaic systems.

Main Methods:

  • An AI-based algorithm was designed to dynamically adjust solar tracker positioning.
  • The algorithm incorporates real-time weather data and optimizes spacing between solar panels.
  • Effectiveness was validated using bifacial panels in a real-world solar plant in northeastern Brazil.

Main Results:

  • The AI algorithm achieved energy gains of up to 7.83% on cloudy days.
  • An average energy gain of approximately 1.2% was observed compared to a commercial solar tracker.
  • The developed methodology proved replicable globally.

Conclusions:

  • AI-driven solar tracking algorithms can significantly enhance energy yield.
  • Accounting for dynamic factors like weather and panel proximity is crucial for maximizing PV plant efficiency.
  • The proposed algorithm offers a robust solution for optimizing solar energy generation worldwide.