基于数学形态学和Pearson相关系数的风电变流器开路故障识别方法

doi:10.19595/j.cnki.1000-6753.tces.240752

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摘要

由于风电机组工作环境恶劣,电力电子设备脆弱,变流器故障成为风电机组的常见故障之一。而变流器在风力发电系统中起到优化运行的作用,因此针对变流器开路故障的检测与识别至关重要。现有的检测方法存在鲁棒性不够强以及计算复杂的问题,因此本文提出一种同时基于直流母线电压的和基于转子电流的故障识别方法。该方法基于数学形态学提取直流母线电压故障特征实现故障检测;其次基于Pearson相关系数提取转子侧三相电流波形特征定位故障相,并基于电流平均值定位故障桥臂。该方法计算过程简单,可以准确识别变流器功率管的单管和双管故障,理论推导证明了所提方法的有效性和可靠性。仿真算例考虑了风速波动、负载波动和噪声环境的影响,结果表明所提方法具有较高的准确率及较强的鲁棒性。

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	卢奥煊
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关键词 ：变流器, 开路故障, 数学形态学, Pearson相关系数, 故障识别

Abstract：

The increasing deployment of wind turbines in challenging environments has led to the prevalent issue of converter faults, which significantly affect the reliability and efficiency of wind power systems. Given the critical role that the converter plays in optimizing the wind power conversion process, detecting and identifying open-circuit fault in wind converter is essential for maintaining operational integrity and maximizing energy output. Current fault identification methods often suffer from limitations related to robustness and computational complexity, necessitating improved solutions. To address these shortcomings, this paper introduces an innovative fault identification method that integrates analysis of the direct current (DC) bus voltage and rotor current characteristics. It can accurately recognize the single and double tube faults of converter power tubes.
Firstly,Fault detection is facilitated by the fact that the DC bus voltage signal is easily accessible, independent of the load and control strategy. Extraction of DC bus voltage edge gradients using mathematical morphology as a feature to detect the occurrence of faults. Secondly, the Pearson correlation coefficients of the rotor side currents are calculated to analyze waveform characteristics. The coupling relationship between the three-phase currents is theoretically deduced, and it is proved that the Pearson correlation coefficients between the two-phase currents are significantly different under different fault conditions, enabling precise identification of the fault phase. Moreover, the location of the fault bridge arm is determined using the average value of the current, enhancing the accuracy of fault identification. Finally, the decision function is used to locate the faulty power tube and realize the fault classification.
Simulation results of the open-circuit fault model of doubly-fed wind power converter show that the proposed method in this paper can accurately determine the occurrence of faults and locate the position of power tubes. By comparing under large data sets, it is found that the proposed method improves the accuracy while maintaining a shorter detection time compared to other methods, which is more practical and reliable. The simulation results show that the wind speed fluctuation has a negligible effect on the DC bus voltage and rotor current, and no fault occurrence is detected, while the current characteristics are stabilized in the range of the fault-free case, which indicates that the proposed method can overcome the interference of wind speed fluctuation. By simulating voltage dips to model the load fluctuations, it is found that the fault detection module misjudges the occurrence of faults, and the current characteristics is small affected but similar in size. It is judged that no faults have occurred, so the fault identification module can be used as a verification of fault detection. A Gaussian white noise with a signal-to-noise ratio of 20 dB is also added to the acquired voltage and current data, and the results show that the proposed method is not disturbed by noise.
The following conclusions can be drawn from the simulation analysis: (1) Compared with existing methods, the method is not only simple and effective in calculation, but also has a higher accuracy rate. (2) The fault detection method based on mathematical morphology utilizes the DC bus voltage, which is easy to obtain data and rapid to detect, and is not affected by noise. (3) The Pearson correlation coefficient-based fault classification method classifies the rotor three-phase currents according to their waveform correlation, and the consistency of theoretical and simulation results shows that the method is effective and of practical significance, and the method has strong robustness.

Key words： Converter Open circuit fault Fault identification Mathematical morphology Pearson correlation coefficient

收稿日期: 2024-05-10

PACS:

TM46

基金资助:

国家自然科学基金（52077081）和广东省基础与应用基础研究基金自然科学基金（2022A1515011608）资助项目。

通讯作者: 季天瑶女,1981年生,教授,博士生导师,研究方向为电力系统信号处理。E-mail：tyji@scut.edu.cn

作者简介: 卢奥煊男,2000年生,硕士研究生,研究方向为风电变流器故障诊断及信号处理。E-mail：202221015205@mail.scut.edu.cn

引用本文:

卢奥煊, 季天瑶, 李梦诗, 莫春, 郑欣. 基于数学形态学和Pearson相关系数的风电变流器开路故障识别方法[J]. 电工技术学报, 0, (): 20240752-20240752. Lu Aoxuan, Ji Tianyao, Li Mengshi, Mo Chun, Zheng Xin. Identification of Open-Circuit Fault in Wind Power Converter Based on Mathematical Morphology and Pearson Correlation Coefficient. Transactions of China Electrotechnical Society, 0, (): 20240752-20240752.

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