Distribution transformers play a vital role in power distribution systems,and their reliable operation is crucial for grid stability.This study presents a simulation-based framework for active fault diagnosis and earl...Distribution transformers play a vital role in power distribution systems,and their reliable operation is crucial for grid stability.This study presents a simulation-based framework for active fault diagnosis and early warning of distribution transformers,integrating Sample Ensemble Learning(SEL)with a Self-Optimizing Support Vector Machine(SO-SVM).The SEL technique enhances data diversity and mitigates class imbalance,while SO-SVM adaptively tunes its hyperparameters to improve classification accuracy.A comprehensive transformer model was developed in MATLAB/Simulink to simulate diverse fault scenarios,including inter-turn winding faults,core saturation,and thermal aging.Feature vectors were extracted from voltage,current,and temperature measurements to train and validate the proposed hybrid model.Quantitative analysis shows that the SEL–SO-SVM framework achieves a classification accuracy of 97.8%,a precision of 96.5%,and an F1-score of 97.2%.Beyond classification,the model effectively identified incipient faults,providing an early warning lead time of up to 2.5 s before significant deviations in operational parameters.This predictive capability underscores its potential for preventing catastrophic transformer failures and enabling timely maintenance actions.The proposed approach demonstrates strong applicability for enhancing the reliability and operational safety of distribution transformers in simulated environments,offering a promising foundation for future real-time and field-level implementations.展开更多
针对电力物联网环境下配电变压器状态监测面临的低功耗、可靠传输及数据融合等技术难题,设计基于窄带物联网(Narrow Band Internet of Things,NB-IoT)的配电变压器状态监测系统。该系统采用省电模式(Power Saving Mode,PSM)休眠控制策...针对电力物联网环境下配电变压器状态监测面临的低功耗、可靠传输及数据融合等技术难题,设计基于窄带物联网(Narrow Band Internet of Things,NB-IoT)的配电变压器状态监测系统。该系统采用省电模式(Power Saving Mode,PSM)休眠控制策略降低终端能耗,利用受限应用协议(Constrained Application Protocol,CoAP)及重传机制保障数据可靠传输,并引入加权融合计算实现多源异构数据的协同诊断。研究结果表明,该系统能够有效满足配电变压器长期稳定运行与精准状态评估的需求。展开更多
文摘Distribution transformers play a vital role in power distribution systems,and their reliable operation is crucial for grid stability.This study presents a simulation-based framework for active fault diagnosis and early warning of distribution transformers,integrating Sample Ensemble Learning(SEL)with a Self-Optimizing Support Vector Machine(SO-SVM).The SEL technique enhances data diversity and mitigates class imbalance,while SO-SVM adaptively tunes its hyperparameters to improve classification accuracy.A comprehensive transformer model was developed in MATLAB/Simulink to simulate diverse fault scenarios,including inter-turn winding faults,core saturation,and thermal aging.Feature vectors were extracted from voltage,current,and temperature measurements to train and validate the proposed hybrid model.Quantitative analysis shows that the SEL–SO-SVM framework achieves a classification accuracy of 97.8%,a precision of 96.5%,and an F1-score of 97.2%.Beyond classification,the model effectively identified incipient faults,providing an early warning lead time of up to 2.5 s before significant deviations in operational parameters.This predictive capability underscores its potential for preventing catastrophic transformer failures and enabling timely maintenance actions.The proposed approach demonstrates strong applicability for enhancing the reliability and operational safety of distribution transformers in simulated environments,offering a promising foundation for future real-time and field-level implementations.
文摘针对电力物联网环境下配电变压器状态监测面临的低功耗、可靠传输及数据融合等技术难题,设计基于窄带物联网(Narrow Band Internet of Things,NB-IoT)的配电变压器状态监测系统。该系统采用省电模式(Power Saving Mode,PSM)休眠控制策略降低终端能耗,利用受限应用协议(Constrained Application Protocol,CoAP)及重传机制保障数据可靠传输,并引入加权融合计算实现多源异构数据的协同诊断。研究结果表明,该系统能够有效满足配电变压器长期稳定运行与精准状态评估的需求。
