Diagnosis method for open‐circuit faults of six‐phase permanent magnet synchronous motor drive system

Zhang, Zhifeng; Wu, Yue; Qi, Siyu

doi:10.1049/iet-pel.2019.1594

Cited by 7 publications

(4 citation statements)

References 32 publications

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“…When the current estimated value 𝚤𝚤′ is consistent with the current value of the reference model, and the state of the adjustable model is the same as that of the reference model, the estimated speed is basically the same as the real speed [25,26]. Therefore, the estimated speed can be adjusted through the current error to make the speed error zero.…”

Section: Design Of Sensorless Control System For Permanent Magnet Syn...mentioning

confidence: 92%

Vector Control of Permanent Magnet Synchronous Motor Based on MRAS Method

2022

IJM

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In recent years, the application range of electric energy in modern industry has gradually expanded. Permanent magnet synchronous motor has the characteristics of high efficiency and energy saving and has obvious advantages in traction application. In order to achieve good vector control of permanent magnet synchronous motor in the full speed range, Research on model reference adaptive system (MRAS) and pulsating high frequency injection method to construct the permanent magnet synchronous motor vector control system and combined with the flux weakening control algorithm to control the motor under the condition of limited inverter output, so as to realize the sensorless vector control of the motor in the full speed range. The difference between the estimated value of the algorithm and the actual value is compared on the actual experimental platform to verify the feasibility of the control algorithm. The experimental results show that, under medium and high-speed working conditions, the motor speed and rotor position tracking accuracy of MRAS algorithm is high, and the tracking error is less than 0.01rad. The pulsating high-frequency injection method can accurately track the permanent magnet synchronous motor under lowspeed working conditions, the speed error is less than 1n / R / min, and the rotor position error is less than 0.03rad. The static and dynamic performance of the control system is good, It can better deal with the sudden change of motor load. Using MRAS algorithm and pulsating high frequency injection method to control permanent magnet synchronous motor in full speed range is of great significance to improve the speed control performance of permanent magnet synchronous motor.

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Section: Design Of Sensorless Control System For Permanent Magnet Syn...mentioning

confidence: 92%

Vector Control of Permanent Magnet Synchronous Motor Based on MRAS Method

2022

IJM

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“…For example, a 128*128 picture has 16,384 eigenvalues, while the number of independent eigenvalues of power system fault data is only five. When processing power system fault data, the learning ability of neural network may be greatly reduced due to the small number of input eigenvalues and the mismatch of dimensions (Zhang, Z. F, et al, 2020). Aiming at the problem that there are few eigenvalues of power system fault data, the paper makes corresponding improvements.…”

Section: Fault Diagnosis and Prediction Model Of Power Systemmentioning

confidence: 99%

Power System Fault Diagnosis and Prediction System Based on Graph Neural Network

Hao,

Zhang,

Ping

2024

International Journal of Information Technologies and Systems Approach

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The stability and reliability of the power system are of utmost significance in upholding the smooth functioning of modern society. Fault diagnosis and prediction represent pivotal factors in the operation and maintenance of the power system. This article presents an approach employing graph neural network (GNN) to enhance the precision and efficiency of power system fault diagnosis and prediction. The system's efficacy lies in its ability to capture the intricate interconnections and dynamic variations within the power system by conceptualizing it as a graph structure and harnessing the capabilities of GNN. In this study, the authors introduce a substitution for the pooling layer with a convolution operation. A central role is played by the global average pooling layer, connecting the convolution layer and the fully connected layer. The fully connected layer carries out nonlinear computations, ultimately providing the classification at the top-level output layer. In experiments and tests, we verified the performance of the system.

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“…And the fault characteristic frequency is near the power frequency [39]. Therefore, the fault types of the motor can be judged by analyzing the frequency characteristics of the low-frequency component in the current signal [40]. The discrete wavelet decomposition algorithm can transform time domain data to frequency domain data.…”

Section: Preprocessing Of Current Signal Based On Wavelet Decompositionmentioning

confidence: 99%

A Novel Motor Fault Diagnosis Method Based on Generative Adversarial Learning with Distribution Fusion of Discrete Working Conditions

Lan¹,

Chen²,

Yao³

2023

Computer Modeling in Engineering &Amp; Sciences

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Many kinds of electrical equipment are used in civil and building engineering. The motor is one of the main power components of this electrical equipment, which can provide stable power output. During the long-term use of motors, various motor faults may occur, which affects the normal use of electrical equipment and even causes accidents. It is significant to apply fault diagnosis for the motors at the construction site. Aiming at the problem that signal data of faulty motor lack diversity, this research designs a multi-layer perceptron Wasserstein generative adversarial network, which is used to enhance training data through distribution fusion. A discrete wavelet decomposition algorithm is employed to extract the low-frequency wavelet coefficients from the original motor current signals. These are used to train the multi-layer perceptron Wasserstein generative adversarial model. Then, the trained model is applied to generate fake current wavelet coefficients with the fused distribution. A motor fault classification model consisting of a feature extractor and pattern recognizer is built based on perceptron. The data augmentation experiment shows that the fake dataset has a larger distribution than the real dataset. The classification model trained on a real dataset, fake dataset and combined dataset achieves 21.5%, 87.2%, and 90.1% prediction accuracy on the unseen real data, respectively. The results indicate that the proposed data augmentation method can effectively generate fake data with the fused distribution. The motor fault classification model trained on a fake dataset has better generalization performance than that trained on a real dataset.

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Diagnosis method for open‐circuit faults of six‐phase permanent magnet synchronous motor drive system

Cited by 7 publications

References 32 publications

Vector Control of Permanent Magnet Synchronous Motor Based on MRAS Method

Vector Control of Permanent Magnet Synchronous Motor Based on MRAS Method

Power System Fault Diagnosis and Prediction System Based on Graph Neural Network

A Novel Motor Fault Diagnosis Method Based on Generative Adversarial Learning with Distribution Fusion of Discrete Working Conditions

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