In this article, an inter-turn short-circuit(ITSC) fault diagnosis and severity estimation method based on extended state observer(ESO) and convolutional neural network(CNN) is proposed for five-phase permanent magnet...In this article, an inter-turn short-circuit(ITSC) fault diagnosis and severity estimation method based on extended state observer(ESO) and convolutional neural network(CNN) is proposed for five-phase permanent magnet synchronous motor(PMSM) drives. The relationship between fault parameters and motor parameters is analyzed and the equivalent model of ITSC faults in the natural reference frame is accordingly derived. To achieve fault detection and location, the short-circuit turn ratio and short-circuit current are integrated as the fault diagnosis index. According to the model of the shortcircuit current, an ESO is designed for the estimation of the fault diagnosis index. Further, the sensitivity analysis among fault parameters is conducted to evaluate the short-circuit turn ratio and the short-circuit resistance. Subsequently, the postfault current, back electromotive force, electrical angular velocity, q1-axis current reference and the fault diagnosis index are selected as the input signals of CNN to estimate the short-circuit turn ratio. This approach not only resolves parameter coupling challenges but also provides a quantitative assessment of fault severity. Finally, simulations and experiments under different operating points validate the effectiveness of the proposed method.展开更多
Synchronous generators are important components of power systems and are necessary to maintain its normal and stable operation.To perform the fault diagnosis of mild inter-turn short circuit in the excitation winding ...Synchronous generators are important components of power systems and are necessary to maintain its normal and stable operation.To perform the fault diagnosis of mild inter-turn short circuit in the excitation winding of a synchronous generator,a gate recurrent unit-convolutional neural network(GRU-CNN)model whose structural parameters were determined by improved particle swarm optimization(IPSO)is proposed.The outputs of the model are the excitation current and reactive power.The total offset distance,which is the fusion of the offset distance of the excitation current and offset distance of the reactive power,was selected as the fault judgment criterion.The fusion weights of the excitation current and reactive power were determined using the anti-entropy weighting method.The fault-warning threshold and fault-warning ratio were set according to the normal total offset distance,and the fault warning time was set according to the actual situation.The fault-warning time and fault-warning ratio were used to avoid misdiagnosis.The proposed method was verified experimentally.展开更多
The intent of this paper is to analyze the electromagnetic signature of stator winding inter-turn short-circuit fault in a closed loop speed controlled Induction Motor(IM)employing Finite Element Method.Stator winding...The intent of this paper is to analyze the electromagnetic signature of stator winding inter-turn short-circuit fault in a closed loop speed controlled Induction Motor(IM)employing Finite Element Method.Stator winding short-circuit nearly covers 21%of faults in IM.Diagnosing the inter-turn fault at an incipient stage is one of the challenging task in the area of fault detection of IM to prevent crucial damages in industrial applications.Also detecting the faults in inverter fed IM under variable speed applications under varying load is one of the major issues in industrial drives.As the signatures of electromagnetic field contains the entire data in association with the location of rotor,stator and mechanical parts of the motor,a regular monitoring of fields in the airgap can be used to diagnose the inter-turn fault in the stator winding of IM.In this direction,an IM is modeled with several inter-turn fault severities like 30 turns,15 turns,5 turns&1 turn short using ANSYS Maxwell FEA tool and coupled with ANSYS Simplorer for loading arrangements.The PWM inverter with closed loop speed controlled strategy is implemented in Matlab Simulink and co-simulated with ANSYS Simplorer to integrate all the components in one common simulation platform environment for accurate design&analysis for realistic simulation.Several electromagnetic variables like flux density,flux lines and airgap flux density distribution over the machine are analyzed.The spatial FFT spectrum of radial component of flux density in the airgap contains the information related to the diagnosis of inter-turn fault at the incipient stage.展开更多
Inter-turn fault is a serious stator winding short-circuit fault of permanent magnet synchronous machine(PMSM). Once it occurs, it produces a huge short-circuit current that poses a great risk to the safe operation of...Inter-turn fault is a serious stator winding short-circuit fault of permanent magnet synchronous machine(PMSM). Once it occurs, it produces a huge short-circuit current that poses a great risk to the safe operation of PMSM. Thus, an inter-turn short-circuit fault(ITSCF) diagnosis method based on high frequency(HF) voltage residual is proposed in this paper with proper HF signal injection. First, the analytical models of PMSM after the ITSCF are deduced. Based on the model, the voltage residual at low frequency(LF) and HF can be obtained. It is revealed that the HF voltage residual has a stronger ITSCF detection capability compared to the LF voltage residual. To obtain optimal fault signature, a 3-phase symmetrical HF voltage is injected into the machine drive system, and the HF voltage residuals are extracted. The fault indicator is defined as the standard deviation of the 3-phase HF voltage residuals. The effectiveness of the proposed ITSCF diagnosis method is verified by experiments on a triple 3-phase PMSM. It is worth noting that no extra hardware equipment is required to implement the proposed method.展开更多
This paper proposed a new diagnosis model for the stator inter-turn short circuit fault in synchronous generators.Different from the past methods focused on the current or voltage signals to diagnose the electrical fa...This paper proposed a new diagnosis model for the stator inter-turn short circuit fault in synchronous generators.Different from the past methods focused on the current or voltage signals to diagnose the electrical fault,the sta-tor vibration signal analysis based on ACMD(adaptive chirp mode decomposition)and DEO3S(demodulation energy operator of symmetrical differencing)was adopted to extract the fault feature.Firstly,FT(Fourier trans-form)is applied to the vibration signal to obtain the instantaneous frequency,and PE(permutation entropy)is calculated to select the proper weighting coefficients.Then,the signal is decomposed by ACMD,with the instan-taneous frequency and weighting coefficient acquired in the former step to obtain the optimal mode.Finally,DEO3S is operated to get the envelope spectrum which is able to strengthen the characteristic frequencies of the stator inter-turn short circuit fault.The study on the simulating signal and the real experiment data indicates the effectiveness of the proposed method for the stator inter-turn short circuit fault in synchronous generators.In addition,the comparison with other methods shows the superiority of the proposed model.展开更多
Aiming at the fact that the rotor winding inter-turn weak faults can hardly be detected due to the strong electromagnetic coupling effect in the excitation system,an interval observer based on current residual is desi...Aiming at the fact that the rotor winding inter-turn weak faults can hardly be detected due to the strong electromagnetic coupling effect in the excitation system,an interval observer based on current residual is designed.Firstly,the mechanism of the inter-turn short circuit of the rotor winding in the excitation system is modeled under the premise of stable working conditions,and electromagnetic decoupling and system simplification are carried out through Park Transform.An interval observer is designed based on the current residual in the two-phase coordinate system,and the sensitive and stable conditions of the observer is preset.The fault diagnosis process based on the interval observer is formulated,and the observer gain matrix is convexly optimized by linear matrix inequality.The numerical simulation and experimental results show that the inter-turn short circuit weak fault is hardly detected directly through the current signal,but the fault is quickly and accurately diagnosed through the residual internal observer.Compared with the traditional fault diagnosis method based on excitation current,the diagnosis speed and accuracy are greatly improved,and the probability of misdiagnosis also decreases.This method provides a theoretical basis for weak fault identification of excitation systems,and is of great significance for the operation and maintenance of excitation systems.展开更多
This work proposes an alternative strategy to the use of a speed sensor in <span style="white-space:normal;font-size:10pt;font-family:;" "="">the implementation of active and reactive po...This work proposes an alternative strategy to the use of a speed sensor in <span style="white-space:normal;font-size:10pt;font-family:;" "="">the implementation of active and reactive power based model reference adaptive system (PQ-MRAS) estimator in order to calculate the rotor and stator resistances of an induction motor (IM) and the use of these parameters for the detection of inter-turn short circuits (ITSC) faults in the stator of this motor. The rotor and stator resistance estimation part of the IM is performed by the PQ-MRAS method in which the rotor angular velocity is reconstructed from the interconnected high gain observer (IHGO). The ITSC fault detection part is done by the derivation of stator resistance estimated by the PQ-</span><span style="white-space:normal;font-size:10pt;font-family:;" "="">MRAS estimator. In addition to the speed sensorless detection of ITSC faults of the IM, an approach to determine the number of shorted turns based on the difference between the phase current of the healthy and faulty machine is proposed. Simulation results obtained from the MATLAB/Simulink platform have shown that the PQ-MRAS estimator using an interconnected high-</span><span style="white-space:normal;font-size:10pt;font-family:;" "="">gain observer gives very similar results to those using the speed sensor. The </span><span style="white-space:normal;font-size:10pt;font-family:;" "="">estimation errors in the cases of speed variation and load torque are al</span><span style="white-space:normal;font-size:10pt;font-family:;" "="">mos</span><span style="white-space:normal;font-size:10pt;font-family:;" "="">t identical. Variations in stator and rotor resistances influence the per</span><span style="white-space:normal;font-size:10pt;font-family:;" "="">formance of the observer and lead to poor estimation of the rotor resistance. The results of ITSC fault detection using IHGO are very similar to the results in the literature using the same diagnostic approach with a speed sensor.</span>展开更多
针对长定子直线同步电机(long-stator linear synchronous motor,简称LSLSM)定子绕组匝间短路(stator winding inter-turn short circuit,简称ITSC)故障在强噪声与复杂工况下特征微弱,传统电机电流特性分析方法难以实现早期精准检测的问...针对长定子直线同步电机(long-stator linear synchronous motor,简称LSLSM)定子绕组匝间短路(stator winding inter-turn short circuit,简称ITSC)故障在强噪声与复杂工况下特征微弱,传统电机电流特性分析方法难以实现早期精准检测的问题,提出了一种基于迭代自适应多重同步压缩变换(iterative adaptive multiple synchronous compression of transform,简称IAMST)的时频分析方法,通过最优路径搜索与时频脊线压缩提升故障特征能量聚集性,实现LSLSM定子绕组故障的可靠识别。首先,建立常导高速磁浮机-电-磁耦合模型,揭示ITSC故障下d轴电流残差信号以二次谐波为主导的故障特征机理,并分析其对车辆牵引性能的影响;其次,提出IAMST算法,采用改进型Viterbi算法结合动态指数平滑预测,实现强噪声环境下瞬时频率(instantaneous frequency,简称IF)的精准初始化;然后,通过迭代压缩细化机制,将时频能量聚焦于真实IF脊线,克服传统方法的交叉干扰与噪声敏感性问题;最后,基于硬件在环(hardware-in-the-loop,简称HIL)平台开展故障注入实验。结果表明,IAMST方法为非平稳信号时频分析提供了一种抗噪性强、特征分辨率高的解决方案,为磁浮列车LSLSM定子绕组早期故障检测提供了可靠的技术支撑。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
Inter-turn short circuit of field windings is a common electrical fault of generators.Simulation is an important method of investigating the fault and providing data support for fault monitoring.However,huge numbers o...Inter-turn short circuit of field windings is a common electrical fault of generators.Simulation is an important method of investigating the fault and providing data support for fault monitoring.However,huge numbers of pole pairs and damper loops in large hydro-generators would lead to lengthy calculation time,hindering scientific research and engineering application.To deal with this problem,we analyze a theoretical basis for a damper winding simplified model and then propose an equivalent treatment method.Through the analysis of steady-state current harmonic characteristics of generators with different stator winding configurations during the fault,the simplified models suitable for steady-state calculation are derived from two aspects,namely,additional rotor harmonic current frequency characteristics and the relationship of the amplitude as well as the phase of each branch current of the stator.The calculation and experimental results of the two simplified models are then compared to verify the models' correctness.A calculation example of the Three Gorges left bank VGS generator shows few deviations between the calculation results of the simplified model and the original model.Moreover,the calculation time using the simplified model is 1/1500 that using the original model,which provides a more effective tool for on-line fault monitoring.Finally,the sensitivity-verification application of the fault-monitoring scheme based on the stator steady-state unbalanced current RMS is depicted.The result shows that the scheme can monitor two-turn short circuits of field windings in the Three Gorges generator and provide high sensitivity.展开更多
【目的】干式空心电抗器(dry-type air-core reactor,DAR)在运行中易发生匝间短路故障,该类故障具有隐蔽性强、难以实时察觉的特点,对电网的安全稳定运行构成严重威胁。工程中广泛采用的基于磁感应强度幅值的检测方法存在局限性,其对内...【目的】干式空心电抗器(dry-type air-core reactor,DAR)在运行中易发生匝间短路故障,该类故障具有隐蔽性强、难以实时察觉的特点,对电网的安全稳定运行构成严重威胁。工程中广泛采用的基于磁感应强度幅值的检测方法存在局限性,其对内层绕组匝间短路的响应不灵敏,且在多台DAR并列运行时,邻近设备产生的磁场干扰会显著影响检测准确性。为提升故障识别能力并实现早期预警,本文提出一种基于磁场方向角(magnetic field direction angle,MFDA)的DAR匝间短路检测方法。【方法】基于有限元仿真技术,模拟了单相DAR在正常运行及不同位置发生匝间短路时的周围磁场分布;通过在DAR周围设置多个监测点,采集磁感应强度数据,分析了故障发生前后磁感应强度的变化规律;依据磁场叠加原理,计算并分析了三相DAR中故障相附近各监测点MFDA的时序变化规律;对比单台与多台DAR并列运行的MFDA数据,量化评估了邻近正常运行DAR对故障检测的干扰程度,并优化了监测点的布局。【结果】仿真结果表明,单相DAR发生匝间短路后,其周围的磁感应强度幅值和方向均发生了显著变化;与基于幅值的检测方法相比,MFDA对匝间短路表现出更高的响应灵敏度,能够准确捕捉故障所引起的电磁特性变化;通过分析故障发生前后各监测点MFDA的时序变化并设定故障判定阈值,可实现单相DAR匝间短路的诊断;对于三相DAR并列运行场景,在故障相附近、正常相对称中垂线上设置监测点,可有效减少邻近DAR的磁场干扰,结合MFDA时序特征与故障判定阈值,仍能实现短路故障诊断。【结论】提出的基于MFDA的检测方法能够有效弥补磁感应强度幅值检测法在灵敏度和抗干扰能力方面的不足,为DAR运行状态的实时监测与故障预警提供了一种可靠的技术手段。该方法基于仿真研究,展现出良好的工程应用潜力,对提升电网设备运维水平、保障电力系统安全稳定运行具有实际应用价值。展开更多
The harmonic components of stator winding current in induction motor will change under the condition of stator inter-turn short circuit.According to these characteristics,in this paper,a novel technique based on morph...The harmonic components of stator winding current in induction motor will change under the condition of stator inter-turn short circuit.According to these characteristics,in this paper,a novel technique based on morphological maxlifting scheme is proposed for identification of induction motor stator inter-turn short circuit.A max-lifting scheme is applied to process stator winding currents to extract these characteristics.An indicator,r,is computed to identify the short circuit.The transient model of induction motor is employed to simulate oneturn to six-turn stator inter-turn short circuits in an induction motor.Extensive simulation work has been conducted under normal conditions,abnormal conditions(voltage imbalance and varying load),stator inter-turn short circuit conditions,and conditions of any combinations of the above.The results have shown that the scheme proposed in this paper has a high identification rate for induction motor stator inter-turn short circuit.展开更多
Health condition monitoring of induction motors is important because of their vital role and wide us in a variety of industries.A stator inter-turn fault(SITF)is considered to be the most common electrical failure acc...Health condition monitoring of induction motors is important because of their vital role and wide us in a variety of industries.A stator inter-turn fault(SITF)is considered to be the most common electrical failure according to statisti-cal studies.In this paper,an algorithm for the detection of an SITF is presented.It is based on one of the blind source separation techniques called principal component analysis(PCA).The proposed algorithm uses PCA to discriminate between the faulty components of motor current signatures and motor voltage signatures from other components.The standard deviation of one of the decomposed vectors is used as a statistical SITF criterion.The proposed criterion is robust to non-fault conditions including voltage quality problems and large mechanical load changes as well as harmonic contaminants in the voltage supply.In addition,with a straightforward and low computational burden in the fault detection process,the proposed method is computationally efficient.To evaluate the performance of the proposed method,large numbers of practical and simulation scenarios are considered,and the results confrm the good performance,high degree of accuracy,and good convergence speed of the proposed method.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant 52307056in part by the Natural Science Foundation of Jiangsu Province under Grant BK20210475。
文摘In this article, an inter-turn short-circuit(ITSC) fault diagnosis and severity estimation method based on extended state observer(ESO) and convolutional neural network(CNN) is proposed for five-phase permanent magnet synchronous motor(PMSM) drives. The relationship between fault parameters and motor parameters is analyzed and the equivalent model of ITSC faults in the natural reference frame is accordingly derived. To achieve fault detection and location, the short-circuit turn ratio and short-circuit current are integrated as the fault diagnosis index. According to the model of the shortcircuit current, an ESO is designed for the estimation of the fault diagnosis index. Further, the sensitivity analysis among fault parameters is conducted to evaluate the short-circuit turn ratio and the short-circuit resistance. Subsequently, the postfault current, back electromotive force, electrical angular velocity, q1-axis current reference and the fault diagnosis index are selected as the input signals of CNN to estimate the short-circuit turn ratio. This approach not only resolves parameter coupling challenges but also provides a quantitative assessment of fault severity. Finally, simulations and experiments under different operating points validate the effectiveness of the proposed method.
文摘Synchronous generators are important components of power systems and are necessary to maintain its normal and stable operation.To perform the fault diagnosis of mild inter-turn short circuit in the excitation winding of a synchronous generator,a gate recurrent unit-convolutional neural network(GRU-CNN)model whose structural parameters were determined by improved particle swarm optimization(IPSO)is proposed.The outputs of the model are the excitation current and reactive power.The total offset distance,which is the fusion of the offset distance of the excitation current and offset distance of the reactive power,was selected as the fault judgment criterion.The fusion weights of the excitation current and reactive power were determined using the anti-entropy weighting method.The fault-warning threshold and fault-warning ratio were set according to the normal total offset distance,and the fault warning time was set according to the actual situation.The fault-warning time and fault-warning ratio were used to avoid misdiagnosis.The proposed method was verified experimentally.
文摘The intent of this paper is to analyze the electromagnetic signature of stator winding inter-turn short-circuit fault in a closed loop speed controlled Induction Motor(IM)employing Finite Element Method.Stator winding short-circuit nearly covers 21%of faults in IM.Diagnosing the inter-turn fault at an incipient stage is one of the challenging task in the area of fault detection of IM to prevent crucial damages in industrial applications.Also detecting the faults in inverter fed IM under variable speed applications under varying load is one of the major issues in industrial drives.As the signatures of electromagnetic field contains the entire data in association with the location of rotor,stator and mechanical parts of the motor,a regular monitoring of fields in the airgap can be used to diagnose the inter-turn fault in the stator winding of IM.In this direction,an IM is modeled with several inter-turn fault severities like 30 turns,15 turns,5 turns&1 turn short using ANSYS Maxwell FEA tool and coupled with ANSYS Simplorer for loading arrangements.The PWM inverter with closed loop speed controlled strategy is implemented in Matlab Simulink and co-simulated with ANSYS Simplorer to integrate all the components in one common simulation platform environment for accurate design&analysis for realistic simulation.Several electromagnetic variables like flux density,flux lines and airgap flux density distribution over the machine are analyzed.The spatial FFT spectrum of radial component of flux density in the airgap contains the information related to the diagnosis of inter-turn fault at the incipient stage.
基金supported in part by the Jiangsu Carbon Peak Carbon Neutralization Science and Technology Innovation Special Fund under Grant BE2022032-1National Natural Science Foundation of China under Grant 52277035, Grant 51937006 and Grant 51907028the “SEU Zhishan Young Scholars” Program of Southeast University。
文摘Inter-turn fault is a serious stator winding short-circuit fault of permanent magnet synchronous machine(PMSM). Once it occurs, it produces a huge short-circuit current that poses a great risk to the safe operation of PMSM. Thus, an inter-turn short-circuit fault(ITSCF) diagnosis method based on high frequency(HF) voltage residual is proposed in this paper with proper HF signal injection. First, the analytical models of PMSM after the ITSCF are deduced. Based on the model, the voltage residual at low frequency(LF) and HF can be obtained. It is revealed that the HF voltage residual has a stronger ITSCF detection capability compared to the LF voltage residual. To obtain optimal fault signature, a 3-phase symmetrical HF voltage is injected into the machine drive system, and the HF voltage residuals are extracted. The fault indicator is defined as the standard deviation of the 3-phase HF voltage residuals. The effectiveness of the proposed ITSCF diagnosis method is verified by experiments on a triple 3-phase PMSM. It is worth noting that no extra hardware equipment is required to implement the proposed method.
基金supported in part by the National Natural Science Foundation of China(52177042)Natural Science Foundation of Hebei Province(E2020502031)+1 种基金the Fundamental Research Funds for the Central Universities(2017MS151),Suzhou Social Developing Innovation Project of Science and Technology(SS202134)the Top Youth Talent Support Program of Hebei Province([2018]-27).
文摘This paper proposed a new diagnosis model for the stator inter-turn short circuit fault in synchronous generators.Different from the past methods focused on the current or voltage signals to diagnose the electrical fault,the sta-tor vibration signal analysis based on ACMD(adaptive chirp mode decomposition)and DEO3S(demodulation energy operator of symmetrical differencing)was adopted to extract the fault feature.Firstly,FT(Fourier trans-form)is applied to the vibration signal to obtain the instantaneous frequency,and PE(permutation entropy)is calculated to select the proper weighting coefficients.Then,the signal is decomposed by ACMD,with the instan-taneous frequency and weighting coefficient acquired in the former step to obtain the optimal mode.Finally,DEO3S is operated to get the envelope spectrum which is able to strengthen the characteristic frequencies of the stator inter-turn short circuit fault.The study on the simulating signal and the real experiment data indicates the effectiveness of the proposed method for the stator inter-turn short circuit fault in synchronous generators.In addition,the comparison with other methods shows the superiority of the proposed model.
基金supports from National Science Foundation of China(Grant No.51777121).
文摘Aiming at the fact that the rotor winding inter-turn weak faults can hardly be detected due to the strong electromagnetic coupling effect in the excitation system,an interval observer based on current residual is designed.Firstly,the mechanism of the inter-turn short circuit of the rotor winding in the excitation system is modeled under the premise of stable working conditions,and electromagnetic decoupling and system simplification are carried out through Park Transform.An interval observer is designed based on the current residual in the two-phase coordinate system,and the sensitive and stable conditions of the observer is preset.The fault diagnosis process based on the interval observer is formulated,and the observer gain matrix is convexly optimized by linear matrix inequality.The numerical simulation and experimental results show that the inter-turn short circuit weak fault is hardly detected directly through the current signal,but the fault is quickly and accurately diagnosed through the residual internal observer.Compared with the traditional fault diagnosis method based on excitation current,the diagnosis speed and accuracy are greatly improved,and the probability of misdiagnosis also decreases.This method provides a theoretical basis for weak fault identification of excitation systems,and is of great significance for the operation and maintenance of excitation systems.
文摘This work proposes an alternative strategy to the use of a speed sensor in <span style="white-space:normal;font-size:10pt;font-family:;" "="">the implementation of active and reactive power based model reference adaptive system (PQ-MRAS) estimator in order to calculate the rotor and stator resistances of an induction motor (IM) and the use of these parameters for the detection of inter-turn short circuits (ITSC) faults in the stator of this motor. The rotor and stator resistance estimation part of the IM is performed by the PQ-MRAS method in which the rotor angular velocity is reconstructed from the interconnected high gain observer (IHGO). The ITSC fault detection part is done by the derivation of stator resistance estimated by the PQ-</span><span style="white-space:normal;font-size:10pt;font-family:;" "="">MRAS estimator. In addition to the speed sensorless detection of ITSC faults of the IM, an approach to determine the number of shorted turns based on the difference between the phase current of the healthy and faulty machine is proposed. Simulation results obtained from the MATLAB/Simulink platform have shown that the PQ-MRAS estimator using an interconnected high-</span><span style="white-space:normal;font-size:10pt;font-family:;" "="">gain observer gives very similar results to those using the speed sensor. The </span><span style="white-space:normal;font-size:10pt;font-family:;" "="">estimation errors in the cases of speed variation and load torque are al</span><span style="white-space:normal;font-size:10pt;font-family:;" "="">mos</span><span style="white-space:normal;font-size:10pt;font-family:;" "="">t identical. Variations in stator and rotor resistances influence the per</span><span style="white-space:normal;font-size:10pt;font-family:;" "="">formance of the observer and lead to poor estimation of the rotor resistance. The results of ITSC fault detection using IHGO are very similar to the results in the literature using the same diagnostic approach with a speed sensor.</span>
文摘针对长定子直线同步电机(long-stator linear synchronous motor,简称LSLSM)定子绕组匝间短路(stator winding inter-turn short circuit,简称ITSC)故障在强噪声与复杂工况下特征微弱,传统电机电流特性分析方法难以实现早期精准检测的问题,提出了一种基于迭代自适应多重同步压缩变换(iterative adaptive multiple synchronous compression of transform,简称IAMST)的时频分析方法,通过最优路径搜索与时频脊线压缩提升故障特征能量聚集性,实现LSLSM定子绕组故障的可靠识别。首先,建立常导高速磁浮机-电-磁耦合模型,揭示ITSC故障下d轴电流残差信号以二次谐波为主导的故障特征机理,并分析其对车辆牵引性能的影响;其次,提出IAMST算法,采用改进型Viterbi算法结合动态指数平滑预测,实现强噪声环境下瞬时频率(instantaneous frequency,简称IF)的精准初始化;然后,通过迭代压缩细化机制,将时频能量聚焦于真实IF脊线,克服传统方法的交叉干扰与噪声敏感性问题;最后,基于硬件在环(hardware-in-the-loop,简称HIL)平台开展故障注入实验。结果表明,IAMST方法为非平稳信号时频分析提供了一种抗噪性强、特征分辨率高的解决方案,为磁浮列车LSLSM定子绕组早期故障检测提供了可靠的技术支撑。
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.
基金supported by the National Natural Science Foundation of China (Grant No. 50807027)the China Postdoctoral Science Foundation(Grant No. 2012M520155)the Fundamental Research Funds for the Central Universities (Grant No. 2013JBM081)
文摘Inter-turn short circuit of field windings is a common electrical fault of generators.Simulation is an important method of investigating the fault and providing data support for fault monitoring.However,huge numbers of pole pairs and damper loops in large hydro-generators would lead to lengthy calculation time,hindering scientific research and engineering application.To deal with this problem,we analyze a theoretical basis for a damper winding simplified model and then propose an equivalent treatment method.Through the analysis of steady-state current harmonic characteristics of generators with different stator winding configurations during the fault,the simplified models suitable for steady-state calculation are derived from two aspects,namely,additional rotor harmonic current frequency characteristics and the relationship of the amplitude as well as the phase of each branch current of the stator.The calculation and experimental results of the two simplified models are then compared to verify the models' correctness.A calculation example of the Three Gorges left bank VGS generator shows few deviations between the calculation results of the simplified model and the original model.Moreover,the calculation time using the simplified model is 1/1500 that using the original model,which provides a more effective tool for on-line fault monitoring.Finally,the sensitivity-verification application of the fault-monitoring scheme based on the stator steady-state unbalanced current RMS is depicted.The result shows that the scheme can monitor two-turn short circuits of field windings in the Three Gorges generator and provide high sensitivity.
文摘【目的】干式空心电抗器(dry-type air-core reactor,DAR)在运行中易发生匝间短路故障,该类故障具有隐蔽性强、难以实时察觉的特点,对电网的安全稳定运行构成严重威胁。工程中广泛采用的基于磁感应强度幅值的检测方法存在局限性,其对内层绕组匝间短路的响应不灵敏,且在多台DAR并列运行时,邻近设备产生的磁场干扰会显著影响检测准确性。为提升故障识别能力并实现早期预警,本文提出一种基于磁场方向角(magnetic field direction angle,MFDA)的DAR匝间短路检测方法。【方法】基于有限元仿真技术,模拟了单相DAR在正常运行及不同位置发生匝间短路时的周围磁场分布;通过在DAR周围设置多个监测点,采集磁感应强度数据,分析了故障发生前后磁感应强度的变化规律;依据磁场叠加原理,计算并分析了三相DAR中故障相附近各监测点MFDA的时序变化规律;对比单台与多台DAR并列运行的MFDA数据,量化评估了邻近正常运行DAR对故障检测的干扰程度,并优化了监测点的布局。【结果】仿真结果表明,单相DAR发生匝间短路后,其周围的磁感应强度幅值和方向均发生了显著变化;与基于幅值的检测方法相比,MFDA对匝间短路表现出更高的响应灵敏度,能够准确捕捉故障所引起的电磁特性变化;通过分析故障发生前后各监测点MFDA的时序变化并设定故障判定阈值,可实现单相DAR匝间短路的诊断;对于三相DAR并列运行场景,在故障相附近、正常相对称中垂线上设置监测点,可有效减少邻近DAR的磁场干扰,结合MFDA时序特征与故障判定阈值,仍能实现短路故障诊断。【结论】提出的基于MFDA的检测方法能够有效弥补磁感应强度幅值检测法在灵敏度和抗干扰能力方面的不足,为DAR运行状态的实时监测与故障预警提供了一种可靠的技术手段。该方法基于仿真研究,展现出良好的工程应用潜力,对提升电网设备运维水平、保障电力系统安全稳定运行具有实际应用价值。
基金This work was supported by the Fundamental Research Funds for the Central Universities(2015ZZ019)Guangdong Innovative Research Team Program(No.201001N0104744201).
文摘The harmonic components of stator winding current in induction motor will change under the condition of stator inter-turn short circuit.According to these characteristics,in this paper,a novel technique based on morphological maxlifting scheme is proposed for identification of induction motor stator inter-turn short circuit.A max-lifting scheme is applied to process stator winding currents to extract these characteristics.An indicator,r,is computed to identify the short circuit.The transient model of induction motor is employed to simulate oneturn to six-turn stator inter-turn short circuits in an induction motor.Extensive simulation work has been conducted under normal conditions,abnormal conditions(voltage imbalance and varying load),stator inter-turn short circuit conditions,and conditions of any combinations of the above.The results have shown that the scheme proposed in this paper has a high identification rate for induction motor stator inter-turn short circuit.
文摘Health condition monitoring of induction motors is important because of their vital role and wide us in a variety of industries.A stator inter-turn fault(SITF)is considered to be the most common electrical failure according to statisti-cal studies.In this paper,an algorithm for the detection of an SITF is presented.It is based on one of the blind source separation techniques called principal component analysis(PCA).The proposed algorithm uses PCA to discriminate between the faulty components of motor current signatures and motor voltage signatures from other components.The standard deviation of one of the decomposed vectors is used as a statistical SITF criterion.The proposed criterion is robust to non-fault conditions including voltage quality problems and large mechanical load changes as well as harmonic contaminants in the voltage supply.In addition,with a straightforward and low computational burden in the fault detection process,the proposed method is computationally efficient.To evaluate the performance of the proposed method,large numbers of practical and simulation scenarios are considered,and the results confrm the good performance,high degree of accuracy,and good convergence speed of the proposed method.
