Preventive maintenance in the transformer is performed through a dif-ferential relay protection system,and it protects the transformer from internal and external faults.However,the Current Transformer(CT)in the differ...Preventive maintenance in the transformer is performed through a dif-ferential relay protection system,and it protects the transformer from internal and external faults.However,the Current Transformer(CT)in the differential protec-tion system mal-operates during inrush currents.CT saturates due to magnetizing inrush currents and causes false tripping of the differential relays.Moreover,iden-tification of tripping in protection relay either due to inrush current or internal faults needs to be diagnosed.For the above problem,continuous monitoring of transformer breather and CT terminals with thermal camera helps detect the trip-ping in relay due to inrush or internal fault.The transformer’s internal fault leads to high breathing process in the transformer breather,never for inrush currents.During inrush currents,CT temperature is increased.Continuous monitoring of breather and CT of the transformer through thermal imaging and radiometric pix-els detect the causes of CT saturation and differentiates maloperation.Hybrid wavelet threshold image analytics(HWT-IA)based radiometric pixels analysis of the transformer breather and CT after de-noising provides an accurate result of about 95%for identification of the false tripping of differential protection system of transformer.展开更多
The wavelet transformation is applied to the high current transformer.The high current transformer elaborated in the paper is mainly applied to the measurement of AC/DC high current.The principle of the transformer is...The wavelet transformation is applied to the high current transformer.The high current transformer elaborated in the paper is mainly applied to the measurement of AC/DC high current.The principle of the transformer is the Hall direct measurement principle.The transformer has the following three characteristics:firstly, the effect of the remnant field of the iron core on the measurement is decreased;secondly,because the temperature compensation is adopted,the transformer has good temperature charactreristic;thirdly,be cause the wavelet transfomation technology is adopted,the transformer has the capacity of good antijanming.展开更多
Aiming at the problem of energy storage unit failure in the spring operating mechanism of low voltage circuit breakers(LVCBs).A fault diagnosis algorithm based on an improved Sparrow Search Algorithm(ISSA)optimized Ba...Aiming at the problem of energy storage unit failure in the spring operating mechanism of low voltage circuit breakers(LVCBs).A fault diagnosis algorithm based on an improved Sparrow Search Algorithm(ISSA)optimized Backpropagation Neural Network(BPNN)is proposed to improve the operational safety of LVCB.Taking the 1.5kV/4000A/75kA LVCB as an example.According to the current operating characteristics of the energy storage motor,fault characteristics are extracted based on Empirical Wavelet Transform(EWT).Traditional BPNN has problems such as difficulty adjusting network weights and thresholds,being sensitive to initial weights,and quickly falling into local optimal solutions.The Sparrow Search Algorithm(SSA)with self-adjusting weight factors combined with bidirectional mutations is added to optimize the selection of BPNN hyperparameters.The results show that the ISSA-BPNN can accurately and quickly distinguish six conditions of motor voltage reduction:motor voltage increase,motor voltage decrease,energy storage spring stuck,transmission gear stuck,regular state and energy storage spring not locked.It is suitable for fault diagnosis and detection of the energy storage part of LVCB.展开更多
This paper presents a wavelet-based technique for detection and classification of normal and abnormal conditions that occur on power distribution lines. The proposed technique depends on a sensitive fault detection pa...This paper presents a wavelet-based technique for detection and classification of normal and abnormal conditions that occur on power distribution lines. The proposed technique depends on a sensitive fault detection parameter （denoted DET） calculated from the wavelet multi-resolution decomposition of the three phase currents only. This parameter is fast and sensitive to any small changes in the current signal since it uses the square of the first and second details of the decomposed signals. The simulation results of this study clearly show that the proposed technique can be successfully used to detect and classify not only low-current faults that could not be detected by conventional overcurrent relays but also normal transients like load switching and inrush currents.展开更多
为加快电力系统数字化转型,保证高压直流输电(high voltage direct current,HVDC)系统高质量安全运行,有必要通过智能技术充分挖掘、提炼HVDC系统日常调控、运维等阶段积累的海量数据和丰富管理经验,从而构建知识图谱辅助工作人员对故...为加快电力系统数字化转型,保证高压直流输电(high voltage direct current,HVDC)系统高质量安全运行,有必要通过智能技术充分挖掘、提炼HVDC系统日常调控、运维等阶段积累的海量数据和丰富管理经验,从而构建知识图谱辅助工作人员对故障进行诊断和处理。提出了一种基于小波变换和深度学习的HVDC系统故障诊断方法。首先,采用小波变换将换流站的故障录波数据(单相接地、相间短路和阀组短路)转换为二维时频图像,并采用数据增强技术来进一步扩充样本数据集。然后,利用ResNet50网络来实现HVDC系统的故障诊断。根据实验结果,所提方法在训练集的分类精度为93%,在测试集的分类精度为82%,证明了该方法的有效性,为HVDC系统的故障诊断提供了一种新的可行性路线。为了进一步验证所提方法,将其与GoogleNet、VGG16、AlexNet、SVM、决策树和KNN等方法进行对比,对比实验结果表明,所提方法在HVDC系统故障诊断中的表现更加出色。展开更多
文摘Preventive maintenance in the transformer is performed through a dif-ferential relay protection system,and it protects the transformer from internal and external faults.However,the Current Transformer(CT)in the differential protec-tion system mal-operates during inrush currents.CT saturates due to magnetizing inrush currents and causes false tripping of the differential relays.Moreover,iden-tification of tripping in protection relay either due to inrush current or internal faults needs to be diagnosed.For the above problem,continuous monitoring of transformer breather and CT terminals with thermal camera helps detect the trip-ping in relay due to inrush or internal fault.The transformer’s internal fault leads to high breathing process in the transformer breather,never for inrush currents.During inrush currents,CT temperature is increased.Continuous monitoring of breather and CT of the transformer through thermal imaging and radiometric pix-els detect the causes of CT saturation and differentiates maloperation.Hybrid wavelet threshold image analytics(HWT-IA)based radiometric pixels analysis of the transformer breather and CT after de-noising provides an accurate result of about 95%for identification of the false tripping of differential protection system of transformer.
基金ThispaperissupportedbyNationalNatureScienceFoundationofChina (No 60 1760 2 0 )
文摘The wavelet transformation is applied to the high current transformer.The high current transformer elaborated in the paper is mainly applied to the measurement of AC/DC high current.The principle of the transformer is the Hall direct measurement principle.The transformer has the following three characteristics:firstly, the effect of the remnant field of the iron core on the measurement is decreased;secondly,because the temperature compensation is adopted,the transformer has good temperature charactreristic;thirdly,be cause the wavelet transfomation technology is adopted,the transformer has the capacity of good antijanming.
基金This research was funded by Sichuan Science and Technology Program(2023YFSY0013).
文摘Aiming at the problem of energy storage unit failure in the spring operating mechanism of low voltage circuit breakers(LVCBs).A fault diagnosis algorithm based on an improved Sparrow Search Algorithm(ISSA)optimized Backpropagation Neural Network(BPNN)is proposed to improve the operational safety of LVCB.Taking the 1.5kV/4000A/75kA LVCB as an example.According to the current operating characteristics of the energy storage motor,fault characteristics are extracted based on Empirical Wavelet Transform(EWT).Traditional BPNN has problems such as difficulty adjusting network weights and thresholds,being sensitive to initial weights,and quickly falling into local optimal solutions.The Sparrow Search Algorithm(SSA)with self-adjusting weight factors combined with bidirectional mutations is added to optimize the selection of BPNN hyperparameters.The results show that the ISSA-BPNN can accurately and quickly distinguish six conditions of motor voltage reduction:motor voltage increase,motor voltage decrease,energy storage spring stuck,transmission gear stuck,regular state and energy storage spring not locked.It is suitable for fault diagnosis and detection of the energy storage part of LVCB.
文摘This paper presents a wavelet-based technique for detection and classification of normal and abnormal conditions that occur on power distribution lines. The proposed technique depends on a sensitive fault detection parameter （denoted DET） calculated from the wavelet multi-resolution decomposition of the three phase currents only. This parameter is fast and sensitive to any small changes in the current signal since it uses the square of the first and second details of the decomposed signals. The simulation results of this study clearly show that the proposed technique can be successfully used to detect and classify not only low-current faults that could not be detected by conventional overcurrent relays but also normal transients like load switching and inrush currents.
文摘为加快电力系统数字化转型,保证高压直流输电(high voltage direct current,HVDC)系统高质量安全运行,有必要通过智能技术充分挖掘、提炼HVDC系统日常调控、运维等阶段积累的海量数据和丰富管理经验,从而构建知识图谱辅助工作人员对故障进行诊断和处理。提出了一种基于小波变换和深度学习的HVDC系统故障诊断方法。首先,采用小波变换将换流站的故障录波数据(单相接地、相间短路和阀组短路)转换为二维时频图像,并采用数据增强技术来进一步扩充样本数据集。然后,利用ResNet50网络来实现HVDC系统的故障诊断。根据实验结果,所提方法在训练集的分类精度为93%,在测试集的分类精度为82%,证明了该方法的有效性,为HVDC系统的故障诊断提供了一种新的可行性路线。为了进一步验证所提方法,将其与GoogleNet、VGG16、AlexNet、SVM、决策树和KNN等方法进行对比,对比实验结果表明,所提方法在HVDC系统故障诊断中的表现更加出色。
