ArticleSensors (Basel, Switzerland)2026
Meta-Analysis of Data from Radiometric Partial Discharge Localization Systems.
Article in Sensors (Basel, Switzerland), 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
7 authors.
Funding
Abstract
Partial discharge localization is a crucial step in monitoring high-voltage equipment. Over time, due to data acquisition limitations, signal strength has become a key driver of methods of localization. In the literature, the variety of testing conditions makes it challenging to identify the factors influencing localization accuracy clearly. Furthermore, there is a lack of research using real equipment as a source of partial discharge. This paper presents a meta-analysis of data from monitoring systems used for radiometric localization of partial discharge sources, employing algorithms based on the time difference in arrival and received signal strength indicator. The obtained results show that the data exhibited errors with a high standard deviation, indicating that many studies are conducted under significantly different conditions. The first method is more sensitive to noise and the sampling rate, while the second is influenced by the source type and the number of sensors. Moreover, laboratory experiments performed with a potential transformer exhibiting partial discharge suggest that signal propagation in real equipment might not be omnidirectional, which can affect accuracy. Consequently, results based on controlled sources, where source directionality and obstacles have a significant impact on performance, do not accurately reflect real-world conditions. The obtained results indicate that the more realistic the source, the higher the errors will be, due to the lower uniformity in signal propagation.
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.