Smart control and management for a renewable energy based stand-alone hybrid system

Area of Science:

• Electrical Engineering
• Renewable Energy Systems
• Control Systems

Background:

• Independent hybrid systems require efficient energy management for optimal performance.
• Integrating photovoltaic systems (PVS), wind energy conversion systems (WECS), and battery storage systems (BSS) presents control challenges.
• Smart management is crucial for maximizing energy utilization and ensuring power stability.

Purpose of the Study:

• To develop and evaluate an intelligent control strategy for an independent hybrid renewable energy system.
• To enhance the efficiency of photovoltaic and wind energy conversion through advanced MPPT techniques.
• To ensure stable DC bus voltage and effectively manage diverse AC and DC loads.

Main Methods:

• Implementation of an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based Maximum Power Point Tracking (MPPT) technique for PVS and WECS.
• Utilization of an ANFIS-Proportional-Integral (ANFIS-PI) controller for bidirectional converter control and DC bus voltage stabilization.
• Development of a fuzzy logic-based algorithm for intelligent load management considering battery state, solar radiation, and wind speed.

Main Results:

• The ANFIS-based MPPT technique demonstrated superior performance compared to Perturb and Observe (P&O) and fuzzy logic controller (FLC) methods.
• The ANFIS-PI controller successfully stabilized the DC bus voltage.
• The fuzzy logic load management algorithm ensured full utilization of generated renewable energy.
• Simulations in MATLAB/Simulink confirmed the effectiveness of the proposed control strategies.

Conclusions:

• The proposed intelligent management and control system significantly enhances the efficiency and reliability of hybrid renewable energy systems.
• The ANFIS-based approach offers a robust solution for maximizing energy harvest from PVS and WECS.
• The developed system shows strong potential for application in remote regions requiring autonomous and stable power supply.