ArticleScientific reports2026
A novel intelligent control using RBFNN-class topper optimization for stability enhancement in hybrid standalone microgrid.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper presents an intelligent coordinated control framework for a hybrid standalone microgrid integrating solar photovoltaic, wind, and battery energy storage systems. The proposed Radial Basis Function Neural Network–Class Topper Optimization Algorithm (RBFNN–CTOA) controller combines the online adaptive learning capability of the RBFNN with the supervisory optimization strength of the CTOA to enhance voltage stability and dynamic performance under renewable intermittency and load disturbances. The effectiveness of the proposed control strategy is validated through MATLAB/Simulink simulations under varying operating conditions, including sudden load changes and nonlinear loads. The results demonstrate tight DC-link voltage regulation, fast transient recovery, and low current harmonic distortion in compliance with IEEE 519–2014 standards. Comparative analysis further confirms that the proposed controller outperforms conventional PI and ANN-based controllers in terms of voltage regulation accuracy, power quality, and robustness. In addition, the proposed framework exhibits low computational overhead and real-time feasibility, making it suitable for embedded implementation. Owing to its modular structure and supervised online adaptation, the control strategy supports scalable extension to larger or multi-area microgrids without requiring offline retraining for moderate changes in system capacity.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.