Hattabi Intissar, Kheldoun Aissa, Bradai Rafik, Khettab Soufian, Sabo Aliyu, Belkhier Youcef, Khosravi Nima, Oubelaid Adel
SET Laboratory, Electrical and Control Department, Faculty of Technology, Blida 1 University, 09000, Blida, Algeria.
Laboratory of Signals and Systems, Institute of Electrical and Electronic Engineering, University M'hamed Bougara, 35000, Boumerdes, Algeria.
Sci Rep. 2024 Nov 22;14(1):28971. doi: 10.1038/s41598-024-80154-2.
This study concentrates on the implementation of Marine Predator Algorithm (MPA) scheme for tuning of a power system stabilizer's (PSS's) parameters to damp the low-frequency oscillations in a power system. To this, the single machine infinite bus system (SMIB), the Western System Coordinating Council (WSCC) and the New England 10 machine 39-bus power system are utilized for testing and comparing different metaheuristic algorithms using different fitness functions. Optimal PSS parameters of SMIB test system are validated using CU-SLRT Std, a real-time digital simulator. The comparative studies demonstrate that the MPA optimized PSS yields improvements of up to 98.62% in the Particle Swarm Optimization (PSO) at 69.42%, Whale Optimization Algorithm (WOA) at 71.79%, Flower Pollination Algorithm (FPA) at 72.39%, African vulture optimization algorithm (AVOA) at 78.04%, Wild Horse Optimization (WHO) algorithm at 68.57% under various operating scenarios. The superiority of the MPA optimized PSS has been validated using Hardware-in-the-loop implementation for the SMIB test system.
本研究专注于实施海洋捕食者算法（MPA）方案，用于调整电力系统稳定器（PSS）的参数，以抑制电力系统中的低频振荡。为此，利用单机无穷大系统（SMIB）、西部系统协调委员会（WSCC）和新英格兰10机39节点电力系统，使用不同的适应度函数来测试和比较不同的元启发式算法。使用实时数字模拟器CU-SLRT Std对SMIB测试系统的最优PSS参数进行了验证。对比研究表明，在各种运行场景下，MPA优化的PSS在粒子群优化算法（PSO）中提升高达98.62%，在鲸鱼优化算法（WOA）中提升71.79%，在花授粉算法（FPA）中提升72.39%，在非洲秃鹫优化算法（AVOA）中提升78.04%，在野马优化（WHO）算法中提升68.57%。通过对SMIB测试系统进行硬件在环实现，验证了MPA优化PSS的优越性。
