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<p>High-voltage insulators must be highly reliable because failures can cause serious operational problems and safety risks in power transmission networks. This study presents a classification framework for 15&#xa0;kV insulator degradation utilizing a metaheuristic optimization algorithm and a BiGRU-CNN deep learning model. The suggested hybrid methodology, which combines the bidirectional gated recurrent unit (BiGRU) and the convolutional neural network (CNN) to efficiently capture the temporal dependencies from environmental data, performs better and is more accurate when the hyperparameters are optimized using the Improved Particle Swarm Optimization (IPSO) methodology. Experimental analysis was conducted on a comprehensive dataset of environmental and operational parameters collected from 15&#xa0;kV polymeric insulators under real field conditions. Comprehensive analysis is conducted to compare the performance of the proposed IPSO-BiGRU-CNNs with other state-of-the-art deep learning techniques, including RNN, CNN, GRU, LSTM, BiRNN, BiGRU, and CNN-BiGRU models by various evaluation metrics. The model’s performance is further improved by integrating IPSO algorithms for hyperparameter tuning. The most notable improvements are 23.28% BCE, 19.26% validating BCE, 7.23% CP, 11.54% validating CP, 24.72% MAE, 22.62% validating MAE, 26.28% MSE, 21.98% validating MSE, 5.61% PRE, 5.89% validating PRE, 5.05% REC, and 5.88% validating REC. The results of the experiment prove that IPSO-BiGRU-CNNs perform noticeably better than alternative models in terms of accuracy and dependability, which makes it a strong and efficient option for high-voltage insulator state monitoring and predictive maintenance.</p>

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A deterioration classification framework for insulators using metaheuristic optimization and hybrid machine learning

  • 期刊论文

  • 发表日期：

    2026年6月30日

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  • Thanh-Phuong Nguyen

摘要

High-voltage insulators must be highly reliable because failures can cause serious operational problems and safety risks in power transmission networks. This study presents a classification framework for 15 kV insulator degradation utilizing a metaheuristic optimization algorithm and a BiGRU-CNN deep learning model. The suggested hybrid methodology, which combines the bidirectional gated recurrent unit (BiGRU) and the convolutional neural network (CNN) to efficiently capture the temporal dependencies from environmental data, performs better and is more accurate when the hyperparameters are optimized using the Improved Particle Swarm Optimization (IPSO) methodology. Experimental analysis was conducted on a comprehensive dataset of environmental and operational parameters collected from 15 kV polymeric insulators under real field conditions. Comprehensive analysis is conducted to compare the performance of the proposed IPSO-BiGRU-CNNs with other state-of-the-art deep learning techniques, including RNN, CNN, GRU, LSTM, BiRNN, BiGRU, and CNN-BiGRU models by various evaluation metrics. The model’s performance is further improved by integrating IPSO algorithms for hyperparameter tuning. The most notable improvements are 23.28% BCE, 19.26% validating BCE, 7.23% CP, 11.54% validating CP, 24.72% MAE, 22.62% validating MAE, 26.28% MSE, 21.98% validating MSE, 5.61% PRE, 5.89% validating PRE, 5.05% REC, and 5.88% validating REC. The results of the experiment prove that IPSO-BiGRU-CNNs perform noticeably better than alternative models in terms of accuracy and dependability, which makes it a strong and efficient option for high-voltage insulator state monitoring and predictive maintenance.