⚡🧠 Comparative Study of Neuroevolution and Deep Reinforcement Learning for Voltage Regulation in Power Systems

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Voltage regulation is crucial for ensuring the stability, reliability, and efficiency of modern power systems ✨. With increasing renewable integration, load variations, and distributed generators, traditional control strategies often fall short. This study explores two cutting-edge AI-based methods — Neuroevolution and Deep Reinforcement Learning (DRL) — to enhance intelligent control and dynamic voltage regulation. 🌐⚡

🔹 1️⃣ Introduction to Smart Voltage Regulation

  • 🌩️ Challenges in modern power systems

  • 🌱 Impact of renewables and decentralized grids

  • 🤖 Need for AI-driven voltage control

🔹 2️⃣ Neuroevolution (NE) 🧬

⚙️ Concept

  • Evolutionary algorithms + Neural networks

  • Learns control strategies through biological-inspired mutation & selection

⭐ Strengths

  • No gradient requirement

  • Efficient for searching optimal network architectures

  • Handles non-linear dynamic environments well

⚠️ Limitations

  • High computational effort

  • Slower convergence in large state-space systems

🔹 3️⃣ Deep Reinforcement Learning (DRL) 🧠🎯

⚙️ Concept

  • Agent interacts with environment → learns via reward signals

  • Uses deep neural networks for smart decision-making

⭐ Strengths

  • Adaptability to real-time power system conditions

  • Faster convergence with experience replay

  • Improved scalability for large power networks

⚠️ Limitations

  • Requires large training data

  • Sensitive to hyperparameters

🔹 4️⃣ Application to Voltage Regulation ⚡🔌

  • Controls tap changers, capacitator banks, inverters

  • Enhances stability, reduces voltage deviations

  • NE: Suitable for offline optimization

  • DRL: Suitable for real-time dynamic operations


 

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