当背景谐波电压与负载侧谐波电流在中高压配电网公共耦合点(Point of common coupling,PCC)处共存时,易导致PCC谐波电流超出限值,提出采用基于前馈-反馈复合控制的电力感应调控滤波(Controllably inductive power filtering,CIPF)系统...当背景谐波电压与负载侧谐波电流在中高压配电网公共耦合点(Point of common coupling,PCC)处共存时,易导致PCC谐波电流超出限值,提出采用基于前馈-反馈复合控制的电力感应调控滤波(Controllably inductive power filtering,CIPF)系统治理中高压配电网PCC处谐波,将网侧(PCC)谐波电流反馈控制和负载侧谐波电流前馈控制结合后,可以在提高系统稳定性的同时,提供谐振阻尼、实现对网侧/负载侧谐波的双向主动抑制。首先介绍CIPF系统的拓扑结构及特点;建立前馈-反馈复合控制下系统的谐波域数学模型和单相等效电路,在此基础上,分别研究网侧谐波电流相对于负载侧谐波电流和网侧背景谐波电压的传递特性;设计基于谐波分频检测方法的控制方案并推导出系统传递函数,基于此,进一步探讨控制系数取值与系统滤波性能、稳定性之间的关系;最后,通过仿真模型,验证了所提控制方法下CIPF系统滤波性能的优越性。展开更多
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
文摘当背景谐波电压与负载侧谐波电流在中高压配电网公共耦合点(Point of common coupling,PCC)处共存时,易导致PCC谐波电流超出限值,提出采用基于前馈-反馈复合控制的电力感应调控滤波(Controllably inductive power filtering,CIPF)系统治理中高压配电网PCC处谐波,将网侧(PCC)谐波电流反馈控制和负载侧谐波电流前馈控制结合后,可以在提高系统稳定性的同时,提供谐振阻尼、实现对网侧/负载侧谐波的双向主动抑制。首先介绍CIPF系统的拓扑结构及特点;建立前馈-反馈复合控制下系统的谐波域数学模型和单相等效电路,在此基础上,分别研究网侧谐波电流相对于负载侧谐波电流和网侧背景谐波电压的传递特性;设计基于谐波分频检测方法的控制方案并推导出系统传递函数,基于此,进一步探讨控制系数取值与系统滤波性能、稳定性之间的关系;最后,通过仿真模型,验证了所提控制方法下CIPF系统滤波性能的优越性。
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.
