Fault Diagnosis of Circuit Breaker Energy Storage Mechanism Based on Current-Vibration Entropy Weight Characteristic and Grey Wolf Optimization–Support Vector Machine

Zhao, Shutao; Wang, Erxu

doi:10.1109/access.2019.2924056

Cited by 30 publications

(20 citation statements)

References 11 publications

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“…The load of 12,15,16,20,21,23, and 24 (the load of near critical cluster) was increased by 25%. Using the control method proposed in this article, the other conditions and fault settings of the system were maintained as the same.…”

Section: Simulation Of Load Changing Conditionsmentioning

confidence: 99%

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A simultaneous phase sequence exchange control method based on transient energy function

Huang

2020

Int Trans Electr Energ Syst

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Emergency control systems can ensure the power grid stable operation. Phase sequence exchange (PSE) is a recently developed emergency control technology. In this article, a numerical search algorithm for finding PSE is proposed to reduce the maximum unbalanced energy of the system. This method can quickly identify the generator requiring PSE and obtain its power angle threshold without traversing all critical units. After fault clearance, taking the energy at the closest unstable equilibrium point as the critical energy of PSE, even if it is conservative when assessing the system stability, PSE is still conducive to the system stability. The complex search process of the controlling unstable equilibrium point is thus unnecessary. When the power angle reaches the threshold value, the selected generator PSE at the same time, which reduces the unbalanced energy and restrains the out-of-step system. Three test systems verified the proposed control method is effective.

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Section: Simulation Of Load Changing Conditionsmentioning

confidence: 99%

“…The transfer conductance in the system is also ignored. [21][22][23] The motion equation of each generator relative to the center-of-inertia (COI) is as follows:…”

Section: Change Of System Energy Due To Psementioning

confidence: 99%

A simultaneous phase sequence exchange control method based on transient energy function

Huang

2020

Int Trans Electr Energ Syst

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“…When the signal approaches the Gaussian distribution, its value tends to 3. In previous works, this parameter was applied and proved to be a very sensitive tool to study the characteristics of the vibration signal [14,15]. The kurtosis can be expressed as:…”

Section: Kurtosis Measurementioning

confidence: 99%

“…where, in the Equation (4), n is the kurtosis, () Ex is the expected value of the signal x , m and s are, respectively the mean and standard deviation of the signal x [15].…”

Section: Kurtosis Measurementioning

confidence: 99%

Identification of Stator Winding Insulation Faults in Three-Phase Induction Motors Using MEMS Accelerometers

Schiewaldt

Lucas

Rocha

et al. 2019

The 6th International Electronic Conference on Sensors and Applications

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In recent years, the advancement of the microelectronics industry has allowed for a major expansion in the development of sensor-based equipment and applications, driven primarily by the cost reduction of micro-electro-mechanical systems (MEMS) devices. Currently, using this type of component, it is feasible to develop cost-effective systems aimed at early detection of failures in electrical machines and, in special cases, three-phase induction motors (TIM). These devices, coupled with predictive maintenance records, can prevent unexpected shutdowns due to malfunctions and signal the need for actions to extend the life cycle of the equipment. This is a relevant topic considering that the industrial sector is increasingly seeking for solutions based on non-destructive techniques (NDT) for preventive and predictive fault diagnosis. In this scenario, the objective of this work is to evaluate the application of a low-cost MEMS accelerometer to identify insulation failures in stator windings through vibration analysis. For this purpose, two MEMS accelerometers were coupled on either side of the frame of a TIM. Then, vibration signals were acquired for different types and levels of insulation failures. The data obtained were processed using different metrics such as root mean square (RMS), kurtosis, and skewness. The results allowed us to identify the insulation faults applied to the TIM, confirming the feasibility of applying the low-cost MEMS accelerometer in the vibration analysis for fault diagnosis.

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“…However, for non-stationary and non-linear signals, waveform distortion and poor adaptability are easy to occur. Motivated by the above considerations, VMD [28][29][30][31][32][33] method is applied in signal analysis of HVCBs in this paper. The VMD method has a solid theoretical foundation and good noise robustness [22].…”

Section: Introductionmentioning

confidence: 99%

Mechanical Fault Diagnosis of High Voltage Circuit Breakers Utilizing VMD Based on Improved Time Segment Energy Entropy and a New Hybrid Classifier

Cao

Liu

2020

IEEE Access

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In mechanical fault diagnosis of the high voltage circuit breakers (HVCBs), it is often expected that the fault type should be confirmed in time to avoid delaying the best time for mechanical fault diagnosis. The traditional diagnosis method of HVBC is not so profound for the identification of slight faults and does not consider the impact of the recall rate of fault samples on the fault diagnosis results. In this paper, we propose a method for HVCBs mechanical fault diagnosis utilizing variational mode decomposition (VMD) based on improved time segment energy entropy (ITSEE) and a new hybrid classifier. Firstly, the signal is decomposed into K intrinsic mode functions (IMFs) via VMD to establish a component matrix. Secondly, the ITSEE method is used to calculate the energy entropy of the matrix in time domain and frequency domain, so as to better extract the features of slight fault types. Finally, an optimal hybrid classifier model combined of one-class support vector machine (OCSVM) and probabilistic neural network (PNN) is used to identify four types of vibration signals of HVCBs. The experimental results show that the accuracy of unknown samples is 98.75%, and the recall of fault type is 100%. The experimental results show the effectiveness of the method and have important application value for the diagnosis of HVBCs. INDEX TERMS High voltage circuit breakers, mechanical fault diagnosis, variational mode decomposition, improved time segment energy entropy, one-class support vector machine, probabilistic neural network.

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Fault Diagnosis of Circuit Breaker Energy Storage Mechanism Based on Current-Vibration Entropy Weight Characteristic and Grey Wolf Optimization–Support Vector Machine

Cited by 30 publications

References 11 publications

A simultaneous phase sequence exchange control method based on transient energy function

A simultaneous phase sequence exchange control method based on transient energy function

Identification of Stator Winding Insulation Faults in Three-Phase Induction Motors Using MEMS Accelerometers

Mechanical Fault Diagnosis of High Voltage Circuit Breakers Utilizing VMD Based on Improved Time Segment Energy Entropy and a New Hybrid Classifier

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