Multiple Open-Circuit Fault Diagnosis for Back-to-Back Converter of PMSG Wind Generation System Based on Instantaneous Amplitude Estimation

Xu, Shuiqing; Tao, Songbing; Zheng, Weixing; Chai, Yi; Ma, Mingyao; Ding, Lijian

doi:10.1109/tim.2021.3062683

Cited by 27 publications

(12 citation statements)

References 43 publications

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“…Therefore, fault diagnosis of the wind turbines is of great significance. 3 The power converter plays the role of power transformation and transmission between the wind turbine and the power grid. Poor working conditions of the power converter, high temperature, vibration, corrosive gas, dust, and other factors will cause converter fault.…”

Section: Introductionmentioning

confidence: 99%

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Open circuit fault diagnosis of wind power converter based on VMD energy entropy and time domain feature analysis

Bai,

Li,

et al. 2023

Energy Science & Engineering

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Aiming at the shortcomings of feature extraction and fault identification in fault diagnosis of wind power converters, a novel method for open circuit fault diagnosis of wind power converters based on variational mode decomposition (VMD) energy entropy (EE) and time domain feature analysis (TDFA) is proposed. Primarily, the three‐phase output current at the grid side of the wind power converter is collected as the original signal, and the VMD is used to decompose the original signal into a series of intrinsic mode functions (IMF). To reduce noise interference as much as possible, the Pearson correlation coefficient between each mode component and the original signal under different fault states is analyzed, and the IMF component containing the major failure features is selected to calculate the energy entropy of each component; afterward, according to the Pearson correlation coefficient results, the modal components of the first layer are selected for time domain feature analysis; finally, the feature matrix that combines energy entropy and time domain feature analysis is inputted into the long short‐term memory neural network for training and fault identification. The simulation and experimental results show that the open circuit fault diagnosis method proposed in this paper has high accuracy and robustness.

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Section: Introductionmentioning

confidence: 99%

“…The occurrence of the fault will reduce the productivity of the wind farm and increase the production cost, which may pose a threat to the power grid and even personal safety in serious cases. Therefore, fault diagnosis of the wind turbines is of great significance 3 …”

Section: Introductionmentioning

confidence: 99%

Open circuit fault diagnosis of wind power converter based on VMD energy entropy and time domain feature analysis

Bai,

Li,

et al. 2023

Energy Science & Engineering

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“…This resulted in drawbacks due to misclassification of the transient operating conditions as abnormalities in the system which further led to unintended trips and maintenance of the PV system. In addition to the above methods, the signal processing-techniques, which are based on the time and frequency domain were investigated in the literature to extract the fault feature from the measured converter data [17][18][19]. Further, the neural network-based multiple open-circuit fault diagnosis for photovoltaic inverters is developed in [20].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Approach for Condition Monitoring and Improving Power Output of Photovoltaic Systems

Sobahi¹,

Haque²,

Kurukuru³

et al. 2023

Computers, Materials &Amp; Continua

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Increasing renewable energy targets globally has raised the requirement for the efficient and profitable operation of solar photovoltaic (PV) systems. In light of this requirement, this paper provides a path for evaluating the operating condition and improving the power output of the PV system in a grid integrated environment. To achieve this, different types of faults in grid-connected PV systems (GCPVs) and their impact on the energy loss associated with the electrical network are analyzed. A data-driven approach using neural networks (NNs) is proposed to achieve root cause analysis and localize the fault to the component level in the system. The localized fault condition is combined with a parallel operation of adaptive neurofuzzy inference units (ANFIUs) to develop a power mismatch-based control unit (PMCU) for improving the power output of the GCPV. To develop the proposed framework, a 10-kW single-phase GCPV is simulated for training the NN-based anomaly detection approach with 14 deviation signals. Further, the developed algorithm is combined with the PMCU implemented with the experimental setup of GCPV. The results identified 98.2% training accuracy and 43000 observations/sec prediction speed for the trained classifier, and improved power output with reduced voltage and current harmonics for the grid-connected PV operation.

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“…In addition, the existing methods cannot achieve a good effect when processing signals containing both integer-order faults and fractional-order faults [ 36 , 37 , 38 ]. In multilevel fault diagnosis methods, new methods using adaptive processing of signal samples have been proposed one after another, which can detect and locate multiple faults more efficiently and reliably than traditional fault diagnosis methods [ 39 , 40 ]. As the structure of the neural network gets deeper and deeper, the gradient descent algorithm can use the Kalman filter to adaptively update the neural network.…”

Section: Introductionmentioning

confidence: 99%

Multilevel Fine Fault Diagnosis Method for Motors Based on Feature Extraction of Fractional Fourier Transform

Wen

2022

Sensors

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Motors are the main driving power for equipment operation, and they are also a major factor to promote the development of the motor and the load it drives and its motor control system toward a low-carbon future, reduce carbon emissions, and improve the industrial economy and social economic efficiency. Due to high-speed, long-period, and heavy-load operation, various faults occur; since the existing integer-order Fourier transform methods have not enough able to detect fractional-order faults and lack robustness, it is difficult to realize the fine diagnosis of motor faults, which reduces the safety and reliability of the motor control system. For this reason, on the basis of the powerful extraction ability of the fractional Fourier transform (FRFT) for micro fault features, especially the extraction ability to fit fractional frequency domain faults, this paper intends to establish a multilevel fine fault diagnosis method for fractional-order or integer-order faults. Firstly, this is accomplished by performing the fractional Fourier transform on the acquired data with faults and feature extraction in the multilevel fractional frequency domain and then optimizing the feature extraction model. Secondly, one further step search method is established to determine the projection direction with the largest fault feature. Thirdly, taking the extracted multilevel fault features as input, a multilevel fine fault diagnosis method based on the SVM model is established. Finally, three typical digital simulation examples and actual operating data collected by the ZHS-2 multifunctional motor test bench with a flexible rotor are employed to verify the effectiveness, robustness, and accuracy of this new method. The main contribution and innovation of this paper are that the fractional Fourier transform method based on time domain and frequency domains is introduced. This method can extract the small fault features in the maximum projection direction of the signal in the fractional domain, but detection with other time–frequency methods is difficult; the extracted multilevel fault features are used as input, and the corresponding fault diagnosis model is established, which can improve the accuracy of fault detection and ensure the safe and reliable operation of industrial equipment.

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Multiple Open-Circuit Fault Diagnosis for Back-to-Back Converter of PMSG Wind Generation System Based on Instantaneous Amplitude Estimation

Cited by 27 publications

References 43 publications

Open circuit fault diagnosis of wind power converter based on VMD energy entropy and time domain feature analysis

Open circuit fault diagnosis of wind power converter based on VMD energy entropy and time domain feature analysis

Data-Driven Approach for Condition Monitoring and Improving Power Output of Photovoltaic Systems

Multilevel Fine Fault Diagnosis Method for Motors Based on Feature Extraction of Fractional Fourier Transform

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