Transmission Line Fault Diagnosis Method Based on SDA-ISSA-XGBoost under Meteorological Factors DOI Open Access
Kun Zhang

Journal of Physics Conference Series, Journal Year: 2023, Volume and Issue: 2666(1), P. 012006 - 012006

Published: Dec. 1, 2023

Abstract Transmission lines are directly exposed to the natural environment and prone failure due meteorological factors. A novel approach for diagnosing transmission line faults under various conditions has been introduced. This method, known as SDA-ISSA-XGBoost, combines power of Stacked Denoising Autoencoder (SDA), an improved Sparrow Search Algorithm (ISSA) enhanced with chaotic mapping sequences, adaptive weights, iterative local search, a random differential mutation strategy, eXtreme Gradient Boosting (XGBoost). The process begins SDA, which extracts essential features from initial data. Subsequently, ISSA is applied optimize parameters XGBoost model. results in ISSA-XGBoost fault diagnosis performance this model compared PSO-XGBoost SSA-XGBoost. experimental findings demonstrate that achieves impressive accuracy 94.39%, surpassing both SSA-XGBoost by 6.54% 3.74%, respectively.

Language: Английский

Transmission Line Fault Diagnosis Method Based on SDA-ISSA-XGBoost under Meteorological Factors DOI Open Access
Kun Zhang

Journal of Physics Conference Series, Journal Year: 2023, Volume and Issue: 2666(1), P. 012006 - 012006

Published: Dec. 1, 2023

Abstract Transmission lines are directly exposed to the natural environment and prone failure due meteorological factors. A novel approach for diagnosing transmission line faults under various conditions has been introduced. This method, known as SDA-ISSA-XGBoost, combines power of Stacked Denoising Autoencoder (SDA), an improved Sparrow Search Algorithm (ISSA) enhanced with chaotic mapping sequences, adaptive weights, iterative local search, a random differential mutation strategy, eXtreme Gradient Boosting (XGBoost). The process begins SDA, which extracts essential features from initial data. Subsequently, ISSA is applied optimize parameters XGBoost model. results in ISSA-XGBoost fault diagnosis performance this model compared PSO-XGBoost SSA-XGBoost. experimental findings demonstrate that achieves impressive accuracy 94.39%, surpassing both SSA-XGBoost by 6.54% 3.74%, respectively.

Language: Английский

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