Ethiopia Institute of Technology- Mekelle

Permanent URI for this communityhttps://repository.mu.edu.et/handle/123456789/58

Browse

Filters

20251

Settings

Author: search.filters.author.Rigbey Hailetsion

Search Results

Now showing 1 - 1 of 1
  • No Thumbnail Available
    Item
    Load Frequency Control of Small Hydro Power Plant Based on Artificial Neural Network with Tuned PID Controller
    (Mekelle University, 2025-04-10) Rigbey Hailetsion
    Electricity propels the advancement of society and economy, fueling sectors such as healthcare, education, and industries. Real-time adjustments in power generation are made by load frequency control to stabilize frequency and voltage, a critical factor for uninterrupted power supply. An innovative approach suggests the integration of artificial neural networks and PID controllers for enhancing the performance of small hydropower plants. The core objective of the research is to create an ANN combined with tuned PID for regulating load frequency in small hydro power plants. The thesis elaborates on the limitations of conventional PID controllers and the flexibility of the ANN-based strategy, outlining the process of plant modeling and controller configuration. Both Proportional-Integral-Derivative (PID) controllers and ANN with Tuned PID controllers are commonly employed techniques. While PID controllers offer stability, the ANN with Tuned PID controllers exhibit superior adaptability and quicker responses to dynamic variations, thereby enhancing the efficiency of the SHP. Using the ANN-tuned PID controller results in significant improvements in several areas. The settling time is notably enhanced, decreasing by 74.36% compared to the untuned PID controller and 50.88% compared to the tuned PID controller. The overshoot is greatly reduced, showing a decrease of 96.29% compared to the untuned controller and 90.26% compared to the tuned controller, indicating much better stability. Additionally, the peak time increases slightly by 2.78% compared to the untuned controller and 2.14% compared to the tuned controller, demonstrating minimal delay in reaching the maximum value. These changes highlight faster, more accurate, and stable system responses with advanced tuning techniques.