Journal of Primeasia
Integrative Disciplinary Research | Online ISSN 3064-9870 | Print ISSN 3069-4353
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Volume 2 Number 1 2021
RESEARCH ARTICLE (Open Access)
Intelligent Optimisation for Power System Stability: A Comparative Expert-Survey Analysis of Artificial Neural Networks, Particle Swarm Optimisation, Genetic Algorithms, Fuzzy Logic, and Hybrid AI Approaches
Md Atiqur Rahman1*, Md Shahdat Hossain2
Journal of Primeasia 2 (1) 1-8 https://doi.org/10.25163/primeasia.2110773
Submitted: 26 July 2021 Revised: 19 October 2021 Accepted: 25 October 2021 Published: 27 October 2021
Abstract
Background: The structural transformation of electrical power networks — driven by accelerating renewable energy integration, distributed generation, and increasingly volatile demand patterns — has placed stability management under pressures that conventional linear control methods were not designed to handle. As intermittent sources now account for approximately 30% of generation in many national grids, the case for intelligent optimisation approaches has grown considerably, yet systematic comparative evidence across multiple techniques remains limited.
Methods: This study employed a quantitative, survey-based design combining structured expert elicitation with computational analysis. A validated 18-item Likert-scale questionnaire was administered to power systems professionals with a minimum of five years of relevant experience. Responses were preprocessed using standard data-cleaning protocols and min-max normalisation before being analysed across four intelligent optimisation techniques — Artificial Neural Networks (ANN), Particle Swarm Optimisation (PSO), Genetic Algorithms (GA), and Fuzzy Logic systems — as well as Hybrid AI configurations. Performance was evaluated using a weighted composite stability index across four dimensions: frequency stability, voltage regulation, load balancing, and system recovery.
Results: Renewable variability (15.6%) and load uncertainty (15.0%) emerged as the dominant strategic challenges, with both factors showing strong negative correlations with overall stability scores (r = −0.78 and r = −0.74, respectively). Hybrid AI systems led effectiveness rankings at 17.6%, followed by Deep Learning (16.5%) and ANN (15.9%), while conventional methods trailed at 12.3% — despite recording the highest adoption score (4.22). Optimisation adoption correlated strongly with grid reliability (r = 0.85), the strongest pairwise association in the dataset.
Conclusion: A meaningful gap exists between the methods most widely deployed and those most demonstrably effective. Bridging this divide — through targeted investment, workforce development, and regulatory adaptation — represents one of the more tractable pathways toward resilient, renewable-compatible power infrastructure.
Keywords: Power system stability, Intelligent optimization, Hybrid artificial intelligence, Renewable energy integration, Grid reliability
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