Synergistic Meta-Heuristic Adaptive Real-Time Power System Stabilizer (SMART-PSS)

Classical fixed-parameter power system stabilizers (PSS) are typically designed to work well for a limited and specific set of operating conditions. However, the integration of low-inertia, inverter-based renewable energy resources (RES) has led to rapid fluctuations in power dispatch, rendering non...

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Main Authors: Khaled Aleikish, Jonas Kristiansen Noland, Thomas Oyvang
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Open Access Journal of Power and Energy
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10850756/
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author Khaled Aleikish
Jonas Kristiansen Noland
Thomas Oyvang
author_facet Khaled Aleikish
Jonas Kristiansen Noland
Thomas Oyvang
author_sort Khaled Aleikish
collection DOAJ
description Classical fixed-parameter power system stabilizers (PSS) are typically designed to work well for a limited and specific set of operating conditions. However, the integration of low-inertia, inverter-based renewable energy resources (RES) has led to rapid fluctuations in power dispatch, rendering non-adaptive PSSs obsolete. This paper presents a novel hybrid gray-box modeling approach for real-time adaptation of PSS parameters during operation, thereby enabling the PSS to effectively handle a broader range of operating conditions. In our proposed method, we employ a two-stage process. First, we utilize a modified Heffron-Phillips model and meta-heuristics to synthesize the PSS’s compensating transfer function across a broad spectrum of operating conditions independently of external system parameters. Second, we leverage machine learning techniques to extrapolate the tuning results, thus ensuring adaptability across the full range of operating conditions. The effectiveness of this design methodology is rigorously evaluated in multi-machine power systems. Simulation results demonstrate that the proposed SMART-PSS exhibits robust performance compared to conventional fixed-parameter controllers, reducing the maximum phase deviation by 70% to 96%. This makes it highly suitable for modern power systems, which face diverse and dynamic operational challenges.
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spelling doaj-art-13278af3f36f4a0f92437fe9596eb7852025-01-31T23:05:27ZengIEEEIEEE Open Access Journal of Power and Energy2687-79102025-01-0112364510.1109/OAJPE.2025.353276810850756Synergistic Meta-Heuristic Adaptive Real-Time Power System Stabilizer (SMART-PSS)Khaled Aleikish0https://orcid.org/0009-0001-8757-9259Jonas Kristiansen Noland1https://orcid.org/0000-0002-3656-1032Thomas Oyvang2https://orcid.org/0000-0003-2970-7598Department of Electrical Engineering, IT and Cybernetics, Faculty of Technology, Natural Sciences and Maritime Studies, University of South-Eastern Norway, Porsgrunn, NorwayDepartment of Electric Energy, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, Trondheim, NorwayDepartment of Electrical Engineering, IT and Cybernetics, Faculty of Technology, Natural Sciences and Maritime Studies, University of South-Eastern Norway, Porsgrunn, NorwayClassical fixed-parameter power system stabilizers (PSS) are typically designed to work well for a limited and specific set of operating conditions. However, the integration of low-inertia, inverter-based renewable energy resources (RES) has led to rapid fluctuations in power dispatch, rendering non-adaptive PSSs obsolete. This paper presents a novel hybrid gray-box modeling approach for real-time adaptation of PSS parameters during operation, thereby enabling the PSS to effectively handle a broader range of operating conditions. In our proposed method, we employ a two-stage process. First, we utilize a modified Heffron-Phillips model and meta-heuristics to synthesize the PSS’s compensating transfer function across a broad spectrum of operating conditions independently of external system parameters. Second, we leverage machine learning techniques to extrapolate the tuning results, thus ensuring adaptability across the full range of operating conditions. The effectiveness of this design methodology is rigorously evaluated in multi-machine power systems. Simulation results demonstrate that the proposed SMART-PSS exhibits robust performance compared to conventional fixed-parameter controllers, reducing the maximum phase deviation by 70% to 96%. This makes it highly suitable for modern power systems, which face diverse and dynamic operational challenges.https://ieeexplore.ieee.org/document/10850756/Machine learningmeta-heuristicspower system stabilizersmall signal stability
spellingShingle Khaled Aleikish
Jonas Kristiansen Noland
Thomas Oyvang
Synergistic Meta-Heuristic Adaptive Real-Time Power System Stabilizer (SMART-PSS)
IEEE Open Access Journal of Power and Energy
Machine learning
meta-heuristics
power system stabilizer
small signal stability
title Synergistic Meta-Heuristic Adaptive Real-Time Power System Stabilizer (SMART-PSS)
title_full Synergistic Meta-Heuristic Adaptive Real-Time Power System Stabilizer (SMART-PSS)
title_fullStr Synergistic Meta-Heuristic Adaptive Real-Time Power System Stabilizer (SMART-PSS)
title_full_unstemmed Synergistic Meta-Heuristic Adaptive Real-Time Power System Stabilizer (SMART-PSS)
title_short Synergistic Meta-Heuristic Adaptive Real-Time Power System Stabilizer (SMART-PSS)
title_sort synergistic meta heuristic adaptive real time power system stabilizer smart pss
topic Machine learning
meta-heuristics
power system stabilizer
small signal stability
url https://ieeexplore.ieee.org/document/10850756/
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AT jonaskristiansennoland synergisticmetaheuristicadaptiverealtimepowersystemstabilizersmartpss
AT thomasoyvang synergisticmetaheuristicadaptiverealtimepowersystemstabilizersmartpss