Optimizing weak grid integrated wind energy systems using ANFIS-SRF controlled DSTATCOM.

Area of Science:

• Electrical Engineering
• Renewable Energy Systems
• Intelligent Control Systems

Background:

• Weak grids face challenges integrating wind energy, including voltage fluctuations and harmonic distortions.
• Conventional controllers lack real-time adaptability for dynamic grid conditions.
• Distribution static compensators (DSTATCOMs) are crucial for grid stability but require advanced control.

Purpose of the Study:

• To propose an intelligent control strategy for enhancing power quality in wind energy systems connected to weak grids.
• To develop an adaptive neuro-fuzzy inference system (ANFIS) based synchronous reference frame (SRF) controller for DSTATCOM.
• To demonstrate the superiority of the ANFIS-SRF controller over traditional methods in weak grid scenarios.

Main Methods:

• Implementation of an ANFIS-based SRF control strategy for DSTATCOM.
• Utilizing ANFIS for dynamic adjustment of reactive power compensation, harmonic mitigation, and voltage stabilization.
• Employing simulation to validate the controller's performance under various load and wind conditions.

Main Results:

• Reduced grid voltage total harmonic distortion from 11.26% to 9.83% (non-linear loads) and 4.97% to 2.64% (mixed loads).
• Improved power factor to above 0.98.
• Maintained grid voltage and current stability under varying wind conditions, complying with IEEE 1547 and IEEE 519-2014 standards.

Conclusions:

• The ANFIS-SRF controlled DSTATCOM effectively enhances power quality and stability in weak grids.
• The proposed intelligent control strategy offers superior performance compared to traditional methods for wind energy integration.
• This research contributes a self-learning, real-time adaptive solution for increased grid stability and renewable energy penetration.