Nie Xiujun, Sun Nan, Wang Buqin, Akbari Ganbar
Innovation and Entrepreneurship Institute, Binzhou Polytechnic, Binzhou, 256603, Shandong, China.
Changchun Humanities and Sciences College, Changchun, 130117, Jilin, China.
Sci Rep. 2025 Jul 1;15(1):21663. doi: 10.1038/s41598-025-05547-3.
The purpose of this article is to investigate power system stabilizing (PSS) in multi-machine power systems. In this study, special attention has been given to the role of generator and network modelling which has a direct impact on PSS design. For this purpose, the most important generator models in a power system with several machines in the power network without connection to the infinite bus, and the network connected to the infinite bus have been simulated, and the effects of these models and the infinite bus on the dynamic conditions of the system have been considered. The results of the presented models and the desired network in PSS design have been investigated. To achieve this purpose, an optimal artificial neural network has been utilized, where the parameters of the PID controller are the network output. The network has been optimized by a new promoted version of the firefly algorithm for PSS design and the parameters of this controller in a number of specific working conditions in a multi-machine power system. The method of the optimized neural networks (ANN) has been used for communication and effective use of the parameters obtained through the promoted version of firefly algorithm in a continuous and wide workspace. Numerical simulations considering three-phase short-circuit situations show that ANN/PFF-PSS can decrease load angle overshoot (35.7%) and settling time (28.6%) compared to the conventional PSS. The recovery of voltage is improved also by 9.3%. Through an analysis of systems with and without an infinite bus, the robustness of the proposed stabilizer is validated and shown to be preferred for damping inter-area as well as intra-area oscillations in complicated power networks.
本文旨在研究多机电力系统中的电力系统稳定器（PSS）。在本研究中，特别关注了发电机和网络建模的作用，这对PSS设计有直接影响。为此，对电网中多台发电机且不与无穷大母线相连以及与无穷大母线相连的电力系统中最重要的发电机模型进行了仿真，并考虑了这些模型和无穷大母线对系统动态条件的影响。研究了所提出的模型和PSS设计中所需网络的结果。为实现这一目的，采用了一种优化的人工神经网络，其中PID控制器的参数是网络输出。该网络通过一种改进的萤火虫算法进行优化，用于PSS设计以及多机电力系统中一些特定工况下该控制器的参数。优化神经网络（ANN）的方法已用于在连续且广阔的工作空间中传递和有效利用通过改进的萤火虫算法获得的参数。考虑三相短路情况的数值模拟表明，与传统PSS相比，ANN/PFF-PSS可降低功角超调量（35.7%）和调节时间（28.6%）。电压恢复也提高了9.3%。通过对有无无穷大母线的系统进行分析，验证了所提出的稳定器的鲁棒性，并表明其在复杂电网中抑制区域间和区域内振荡方面更具优势。
