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

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

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Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1 citing paper in PubMed.

Artificial intelligence-driven ensemble deep learning models for smart monitoring of indoor activities in IoT environment for people with disabilities.Scientific reports · 2025
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5 · Who and what money

Authors and funding

8 authors.

Intissar HattabiSET Laboratory, Electrical and Control Department, Faculty of Technology, Blida 1 University, 09000, Blida, Algeria.

Aissa KheldounLaboratory of Signals and Systems, Institute of Electrical and Electronic Engineering, University M'hamed Bougara, 35000, Boumerdes, Algeria.

Rafik BradaiLATSI, Electronics Department, Faculty of Technology, Blida 1 University, 09000, Blida, Algeria.

Soufian KhettabSET Laboratory, Electrical and Control Department, Faculty of Technology, Blida 1 University, 09000, Blida, Algeria.

Aliyu SaboAdvanced Lightning Power and Energy Research (ALPER), Department of Electrical and Electronic Engineering, University Putra Malaysia, Serdang, Malaysia.

Youcef BelkhierUMR CNRS 6027, University of Brest, 29238, Brest, France.

Nima KhosraviDepartment of Electrical and Instrumentation Engineering, R&D Management of NPC, Tehran, Iran. nimakhosravi64@gmail.com.

Adel OubelaidLaboratoire de Technologie Industrielle et de l'Information, Faculté de Technologie, Université de Bejaia, 06000, Bejaia, Algeria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID39578570

DOI10.1007/s13369-012-0423-y

PMCPMC11584892

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.