ArticleScientific reports2025
Power system security and protection considering the integration of new energy power plants.
Article in Scientific reports, 2025. 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
5 authors.
Funding
Abstract
Power quality disturbances (PQDs) pose significant challenges in modern energy power plants-based systems (MEPPBS), especially with the increasing integration of renewable energy sources (RESs) such as wind and solar photovoltaic (PV) systems. The intermittent nature of these sources introduces voltage fluctuations, harmonics, and transient disturbances, affecting grid stability and reliability. This paper presents a novel dual algorithm-based protection approach for detecting, classifying, and mitigating PQDs in grid-connected MEPPBS. The proposed method utilized an advanced adaptive median filter (AMF) as a signal processing-state observer and a support vector machine (SVM)-based scheme to accurately identify disturbances, including voltage sags, swells, harmonics, interruptions, and transients. The proposed dual-algorithm approach, combining AMF and SVM, offers a novel solution that enhances PQD detection accuracy and speed compared to existing methods. Furthermore, the proposed scheme tests five individual PQDs and ten combined disturbances-based datasets, including voltage sags, swells, harmonics, interruptions, and transients were used to train the proposed SVM classifier. Then, SVM-based residuals were calculated by SVM algorithms from estimated AMF data, SVMBR index reveals the detection of PQDs quickly. Simulations were performed in MATLAB® R2022b (Version 9.13) to evaluate the effectiveness of the suggested approach under various operating conditions. The results demonstrate high detection accuracy of 97%, fast response times of less than 15 milliseconds, & robustness in discriminating different PQDs when trained by just 50% of the dataset under a signal-to-noise ratio (SNR) of 20dB. The proposed method achieves 96% precision, 94% recall, and a 0.04 false positive rate, demonstrating high accuracy and reliability in PQDs detection. The findings highlight the potential of the presented method to enhance power system resilience and ensure reliable operation in renewable energy-integrated grids.
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.