1.华北电力大学电子与通信工程系保定071003; 2.华北电力大学河北省电力物联网技术重点实验室 保定071003; 3.华北电力大学保定市光纤传感与光通信技术重点实验室保定071003
TM75TH744
国家自然科学基金（62205105）、河北省自然科学基金（E2019502179）、河北省省级科技计划（SZX2020034）项目资助
- Shang Qiufeng
Shang Qiufeng
1.Department of Electronic and Communication Engineering, North China Electric Power University， Baoding 071003, China; 2.Key Laboratory of Power Internet of Things Technology, Hebei Province, North China Electric Power University， Baoding 071003, China; 3.Key Laboratory of Fiber Optic Sensing and Optical Communication Technology, Baoding City, North China Electric Power University， Baoding 071003, China
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- Fan Xiaokai 1
- Wang Jianjian
Wang Jianjian
1.Department of Electronic and Communication Engineering, North China Electric Power University， Baoding 071003, China; 2.Key Laboratory of Power Internet of Things Technology, Hebei Province, North China Electric Power University， Baoding 071003, China; 3.Key Laboratory of Fiber Optic Sensing and Optical Communication Technology, Baoding City, North China Electric Power University， Baoding 071003, China
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Yao Guozhen
1.Department of Electronic and Communication Engineering, North China Electric Power University， Baoding 071003, China; 2.Key Laboratory of Power Internet of Things Technology, Hebei Province, North China Electric Power University， Baoding 071003, China; 3.Key Laboratory of Fiber Optic Sensing and Optical Communication Technology, Baoding City, North China Electric Power University， Baoding 071003, China
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1.Department of Electronic and Communication Engineering, North China Electric Power University， Baoding 071003, China; 2.Key Laboratory of Power Internet of Things Technology, Hebei Province, North China Electric Power University， Baoding 071003, China; 3.Key Laboratory of Fiber Optic Sensing and Optical Communication Technology, Baoding City, North China Electric Power University， Baoding 071003, China
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基于Φ-OTDR的分布式光纤传感技术，通过分析OPGW中光纤的振动特征，可以实现输电线路全线路、全天候的在线健康监测。采用Φ-OTDR采集了无覆冰、1级覆冰和2级覆冰3种工况下的振动信号，充分挖掘了相位和幅值信号的时域、频域、时频域特征及其对应的统计学特性。为提升覆冰工况的识别准确率，提出基于LCPSO-AdaBoost-MCG的最优特征子集选择算法，以AdaBoost-MCG的分类错误率为适应度函数,采用LCPSO算法迭代计算最优特征子集。采用AdaBoost集成SCN、KNN、PNN、SVM 4种弱分类器，构成了异构强分类器，利用每种弱分类器的优势，提高了模型泛化性能和识别准确率。经现场数据集验证，本文方法对覆冰等级的识别准确率达到了98.7%。根据本文确定的最优特征子集，可以构建覆冰等级预警特征库，为输电线路智能巡检提供有益参考。
Based on Φ-OTDR, distributed fiber optic sensing technology can be utilized to achieve online, real-time health monitoring of power transmission lines by analyzing the vibration characteristics of optical fibers within OPGW. By utilizing Φ-OTDR, vibration signals under three operating conditions—no icing, level 1 icing, and level 2 icing—were collected, and the temporal, frequency, and time-frequency domain features, along with their corresponding statistical properties, of both phase and amplitude signals were thoroughly explored. To enhance the accuracy of icing condition identification, this paper proposes an optimal feature subset selection algorithm based on LCPSO-AdaBoost-MCG. This algorithm employs the classification error rate of the AdaBoost-MCG as the fitness function and iterates with the LCPSO to calculate the optimal feature subset. The AdaBoost ensemble incorporates four weak classifiers: Simple Cartesian Network (SCN), K-Nearest Neighbors (KNN), Probabilistic Neural Network (PNN), and Support Vector Machine (SVM), forming a heterogeneous strong classifier. By leveraging the strengths of each weak classifier, the model′s generalization performance and recognition accuracy are improved. Field data validation demonstrates that the proposed method achieves a 98.7% accuracy in identifying icing levels. Using the optimal feature subset identified in this study, an icing level warning feature library can be established, offering a valuable reference for intelligent transmission line inspection.
引用本文
尚秋峰,谷元宇,樊小凯,王健健,姚国珍.基于LCPSO与异构集成学习模型的输电线路覆冰等级预警方法[J].仪器仪表学报,2024,45(9):157-165
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- 在线发布日期: 2024-12-19
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