Relay Protection Data Judgment and Analysis

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This study introduces a new diagnostic framework that combines improved particle swarm optimization, K-means clustering algorithms, support vector machine (SVM), and learning vector quantization neural networks to provide a comprehensive fault diagnosis and pre-diction model for. Then, due to the particularity of historical statistical data, a weight calculation method combining analytical hierarchy process (AHP) and entropy weight method is adopted to eliminate subjective factors in the weight calculation process. Validation and diagnosis of relay operation is very important to protection engineers in fault analysis.

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Contribute to annontopicmodel/unsupervised_topic_modeling development by creating an account on GitHub.

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Combining the two methods for the assessment of relay protection status allows for the comprehensive consideration of both experts'' subjective

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