利用自适应神经模糊推理辅助优化分数阶 PIDA 控制,为具有太阳能和风能资源的扰动电力系统设计鲁棒 LFC

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Tushar Kanti Roy, Samson S. Yu, Md. Apel Mahmud, Hieu Trinh
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引用次数: 0

摘要

由于结构复杂、电力需求不断增长以及负载干扰,维持现代电力系统的稳定性具有挑战性。可再生能源的整合会导致发电和需求之间的不平衡,从而进一步威胁稳定性。在这种情况下,传统的负载频率稳定方法就显得力不从心。本文提出了一种最优分数阶比例-积分-派生-加速(FOPIDA)控制器,并通过鲁棒性自适应神经模糊推理系统(ANFIS)进行了增强,以改善有风力和太阳能发电机的电力系统的负载频率控制和可靠性。首先,建立了一个多区域互联电力系统的动态模型,其中包括火力发电厂、风力涡轮机和太阳能光伏发电机。然后,针对负载频率控制目标设计了一个分散式 ANFIS-FOPIDA 控制器。该控制器的增益采用鲸鱼优化算法 (WOA) 进行优化,重点关注频率偏差和连接线功率交换。对新英格兰 IEEE 10 发电机 39 总线电力系统的仿真证明了该方法在各种干扰下的有效性,包括随机负载发电干扰和非线性发电行为。与其他策略(如分数阶 (FO) 甲虫群优化算法 (FOBSOA)-FOPIDA、WOA-PIDA 和 WOA-ANFIS-PIDA 以及最新控制方法)的比较突出表明,WOA-ANFIS-FOPIDA 方法在增强电力系统稳定性方面表现出色。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Robust LFC design using adaptive neuro-fuzzy inference-aided optimal fractional-order PIDA control for perturbed power systems with solar and wind power sources

Robust LFC design using adaptive neuro-fuzzy inference-aided optimal fractional-order PIDA control for perturbed power systems with solar and wind power sources

Maintaining stability in modern power systems is challenging due to complex structures, rising power demand, and load disturbances. The integration of renewable energy sources further threatens stability by causing imbalances between generation and demand. Conventional load frequency stabilization methods fall short in such scenarios. This paper proposes an optimal fractional-order proportional-integral-derivative-acceleration (FOPIDA) controller, enhanced by a robust adaptive neuro-fuzzy inference system (ANFIS), to improve load frequency control and reliability in power systems with wind and solar generators. First, the dynamical model of a multi-area interconnected power system, including a thermal power plant, wind turbine, and solar photovoltaic generators, is developed. A decentralized ANFIS-FOPIDA controller is then designed for load frequency control objectives. The gains of this controller are optimized using the whale optimization algorithm (WOA), focusing on frequency deviation and tie-line power exchange. Simulations on a New England IEEE 10-generator 39-bus power system demonstrate the approach's effectiveness under various disturbances, including random load-generation disturbances and nonlinear generation behaviors. Comparisons with other strategies, such as fractional order (FO) beetle swarm optimization algorithm (FOBSOA)-FOPIDA, WOA-PIDA, and WOA-ANFIS-PIDA, and recent control approaches highlight the superior performance of the WOA-ANFIS-FOPIDA method in enhancing power system stability.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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