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
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
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
Due to the harsh actual operating environment of the permanent magnet wind turbine,it is easy to break down and difficult to monitor.Therefore,the electromagnetic characteristics identification of major fault types of...Due to the harsh actual operating environment of the permanent magnet wind turbine,it is easy to break down and difficult to monitor.Therefore,the electromagnetic characteristics identification of major fault types of large-scale permanent magnet wind turbines is studied in this paper.The typical faults of rotor eccentricity,stator winding short circuit and permanent magnet demagnetization of permanent magnet wind turbines are analyzed theoretically.The wavelet analysis algorithm is used to decompose and reconstruct the abnormal electromagnetic signal waveform band,and the characteristic frequency of the electromagnetic signal is obtained when the fault occurs.In order to verify the effectiveness of the proposed method,a 3.680MW permanent magnet wind turbine was taken as the research object.Its physical simulation model was established,and an external circuit was built to carry out field co-simulation.The results show that the motor fault type can be determined by detecting the change rule of fault characteristic frequency in the spectrum diagram,and the electromagnetic characteristic analysis can be applied to the early monitoring of the permanent magnet wind turbine fault.展开更多
To study the effects of wind generators on distribution system protection,the short-circuit current(SCC) characteristics of wind generators is important.Although there are many researches on the issue,a clear agreemen...To study the effects of wind generators on distribution system protection,the short-circuit current(SCC) characteristics of wind generators is important.Although there are many researches on the issue,a clear agreement has not been reached so far.The SCC characteristics for different wind generators are studied.PSCAD simulation is performed in the same system integrated with different kinds of wind generators,and their results are compared with those reported in IEEE papers.The detection possibility by overcurrent relay(OCR)is discussed based on the simulation results.展开更多
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.
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>
基金supported by the National Natural Science Foundation of China(U22A20215 and 51537007)the Natural Science Foundation of LiaoNing Province(2021-YQ-09).
文摘Due to the harsh actual operating environment of the permanent magnet wind turbine,it is easy to break down and difficult to monitor.Therefore,the electromagnetic characteristics identification of major fault types of large-scale permanent magnet wind turbines is studied in this paper.The typical faults of rotor eccentricity,stator winding short circuit and permanent magnet demagnetization of permanent magnet wind turbines are analyzed theoretically.The wavelet analysis algorithm is used to decompose and reconstruct the abnormal electromagnetic signal waveform band,and the characteristic frequency of the electromagnetic signal is obtained when the fault occurs.In order to verify the effectiveness of the proposed method,a 3.680MW permanent magnet wind turbine was taken as the research object.Its physical simulation model was established,and an external circuit was built to carry out field co-simulation.The results show that the motor fault type can be determined by detecting the change rule of fault characteristic frequency in the spectrum diagram,and the electromagnetic characteristic analysis can be applied to the early monitoring of the permanent magnet wind turbine fault.
基金supported by the Power Generation & Electricity Delivery of the Korea Institute of Energy Technology and Planning(KETEP)grant funded by the Korea Government Ministry of Knowledge Economy(No.2009T100200067)
文摘To study the effects of wind generators on distribution system protection,the short-circuit current(SCC) characteristics of wind generators is important.Although there are many researches on the issue,a clear agreement has not been reached so far.The SCC characteristics for different wind generators are studied.PSCAD simulation is performed in the same system integrated with different kinds of wind generators,and their results are compared with those reported in IEEE papers.The detection possibility by overcurrent relay(OCR)is discussed based on the simulation results.