基于海洋捕食者算法的增强型电力系统稳定器整定及对比分析与实时验证

Enhanced power system stabilizer tuning using marine predator algorithm with comparative analysis and real time validation.

作者信息

Hattabi Intissar, Kheldoun Aissa, Bradai Rafik, Khettab Soufian, Sabo Aliyu, Belkhier Youcef, Khosravi Nima, Oubelaid Adel

机构信息

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.

出版信息

Sci Rep. 2024 Nov 22;14(1):28971. doi: 10.1038/s41598-024-80154-2.

DOI:10.1038/s41598-024-80154-2

PMID:39578570

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11584892/

Abstract

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.

摘要

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。