Ramana Peram Venkata, Rosalina K Mercy
Department of EEE, Vignan's Foundation for Science, Technology and Research, Guntur, India.
Sci Rep. 2025 Apr 21;15(1):13662. doi: 10.1038/s41598-025-98872-6.
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.
本文提出了一种基于自适应神经模糊推理系统（ANFIS）的智能控制策略，以提高接入弱电网的风能系统的电能质量。弱电网具有高阻抗和低短路比的特点，在整合风能时会出现电压波动、谐波失真和无功功率不平衡等问题。传统的控制方法，如比例积分控制器和模糊逻辑控制器，缺乏实时适应性，限制了它们在弱电网场景中的有效性。所提出的基于ANFIS的配电静止无功补偿器（DSTATCOM）的同步参考框架（SRF）控制引入了一种智能学习机制，该机制可根据电网条件动态调整无功功率补偿、谐波抑制和电压稳定。与传统方法不同，ANFIS-SRF控制器利用自适应调整和非线性决策能力，确保卓越的系统性能。所获得的仿真结果验证了所提方法的有效性，表明在非线性负载条件下，电网电压总谐波失真从11.26%降低到9.83%，在混合负载场景中从4.97%降低到2.64%，保持符合IEEE 1547和IEEE 519-2014标准。此外，功率因数显著提高，达到0.98以上的值，同时所提出的控制器在变化的风况下成功维持电网电压和电流稳定。这些数值结果确保了ANFIS-SRF控制的DSTATCOM在确保可靠和高效地将风能整合到弱电网方面优于传统控制方法。本研究通过提供一种增强电网稳定性、电能质量和可再生能源渗透率的自学习、实时自适应解决方案，为智能电网控制应用做出了贡献。
