首页|Study Findings on Machine Learning Detailed by Researchers at Institut Teknologi Bandung (Machine learning based multi-method interpretation to enhance dissolve d gas analysis for power transformer fault diagnosis)

Study Findings on Machine Learning Detailed by Researchers at Institut Teknologi Bandung (Machine learning based multi-method interpretation to enhance dissolve d gas analysis for power transformer fault diagnosis)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ar tificial intelligence. According to news originating from Bandung, Indonesia, by NewsRx correspondents, research stated, "Accurate interpretation of dissolved g as analysis (DGA) measurements for power transformers is essential to ensure ove rall power system reliability. Various DGA interpretation techniques have been p roposed in the literature, including the Doernenburg Ratio Method (DRM), Roger R atio Method (RRM), IEC Ratio Method (IRM), Duval Triangle Method (DTM), and Duva l Pentagon Method (DPM)." The news editors obtained a quote from the research from Institut Teknologi Band ung: "While these techniques are well documented and widely used by industry, th ey may lead to different conclusions for the same oil sample. Additionally, the ratio-based methods may result in an out-of-code condition if any of the used ga ses fall outside the specified limits. Incorrect interpretation of DGA measureme nts can lead to mismanagement and may lead to catastrophic consequences for oper ating power transformers. This paper presents a new interpretation technique for DGA aimed at improving its accuracy and consistency. The proposed multi-method approach employs s scoring index and random forest machine learning principles t o integrate existing interpretation methods into one comprehensive technique. Th e robustness of the proposed method is assessed using DGA data collected from se veral transformers under various health conditions."

Institut Teknologi BandungBandungInd onesiaAsiaCyborgsEmerging TechnologiesMachine Learning

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Mar.6)