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

This paper proposes an intelligent control strategy based on the adaptive neuro-fuzzy inference system (ANFIS) to enhance power quality in wind energy systems connected to weak grids. Weak grids, characterized by high impedance and low short-circuit ratios, suffer from voltage fluctuations, harmonic distortions, and reactive power imbalances when integrating wind energy. Conventional control methods, such as proportional-integral and fuzzy logic controllers, lack real-time adaptability, limiting their effectiveness in weak grid scenarios. The proposed ANFIS-based synchronous reference frame (SRF) control for a distribution static compensator (DSTATCOM) introduces an intelligent learning mechanism that dynamically adjusts reactive power compensation, harmonic mitigation, and voltage stabilization based on grid conditions. Unlike traditional approaches, the ANFIS-SRF controller leverages self-adaptive tuning and non-linear decision-making capabilities, ensuring superior system performance. The obtained simulations validate the effectiveness of the proposed method, demonstrating that grid voltage total harmonic distortion is reduced from 11.26 to 9.83% under non-linear load conditions and from 4.97 to 2.64% in mixed-load scenarios, maintaining compliance with IEEE 1547 and IEEE 519-2014 standards. Additionally, the power factor is significantly improved, reaching values above 0.98, while the proposed controller successfully maintains grid voltage and current stability under varying wind conditions. These numerical findings assures that the ANFIS-SRF-controlled DSTATCOM outperforms traditional control methods in ensuring reliable and efficient wind energy integration into weak grids. This study contributes to intelligent grid control applications by providing a self-learning, real-time adaptive solution that enhances grid stability, power quality, and renewable energy penetration.