Optimizing thermal power plant efficiency through washout filter based proportional integral derivative controller.

Area of Science:

• Energy Systems Engineering
• Control Systems
• Artificial Intelligence

Background:

• Improving thermal power plant efficiency is crucial for modern energy demands.
• Existing methods lack comprehensive analysis of temperature and water flow rate control impacts.
• Optimizing these parameters is key to enhancing overall system performance.

Purpose of the Study:

• To investigate the impact of temperature and water flow rate control on thermal power generation efficiency.
• To develop an advanced control system for optimizing thermal power plant operations.
• To enhance system stability and power generation output.

Main Methods:

• Utilized demineralized water and a Proportional Integral Derivative (PID) controller with a Washout Filter (WF).
• Employed Reinforcement Learning based Scaled Gamma Long Short Term Memory (RL-SGLSTM) for PID parameter tuning.
• Implemented Pulse Width Modulation (PWM) for water flow valve control, Spring Vibration Damper (SVD) - Fuzzy Controller (BFC) for frequency stabilization, and Dynamic Voltage Restorer-based Unified Power Flow Controller (DV-UPFC) for voltage sag mitigation.
• Assessed cooling efficiency using Maximum Shifting Correlation Distance-based K-Means Algorithm (MSC-KMA).

Main Results:

• Fine-tuning PID parameters significantly influences system stability and performance.
• Cooling efficiency-driven water flow control enhances thermal power plant operations.
• The proposed method achieved a power generation efficiency of 98%, outperforming existing techniques.

Conclusions:

• The integration of advanced control strategies, including RL-SGLSTM and optimized water flow, substantially boosts thermal power plant efficiency.
• The developed system demonstrates superior performance in terms of stability and energy output.
• This research provides a robust framework for enhancing thermal power generation efficiency.