基于多元高斯分布异常检测模型的MMC子模块开路故障诊断方法

doi:10.19595/j.cnki.1000-6753.tces.220777

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Abstract Modular multilevel converter (MMC) is drawing increasing attention in high-voltage direct- current (HVDC) transmission. Due to the numerous cascaded sub-modules (SMs), safety is one of the most important challenges for MMC. The open-circuit faults for SMs may distort the output voltage and current of MMC and even destroy the system if the faults are not handled in time. Therefore, this paper will concentrate on SM open-circuit faults. In general, the research for fault detection and location (FDL) of SM open-circuit is mainly divided into three basic approaches: (1) sensor-based, (2) model-based, and (3) machine learning (ML)-based. Sensor-based methods can realize FDL quickly but need extra hardware costs, which may also add potential points of fault. Model-based methods are relatively sensitive to uncertain system parameters and environmental noise because of the requirement for an accurate mechanism model of the circuit. This paper proposes a diagnostic strategy based on machine learning (ML) to detect and locate the sub-module open-circuit fault.
Firstly, the operation principle of MMC and SMs are analyzed to find that the capacitor voltages of faulty SMs will be different from the normal capacitance voltage. SM capacitor voltages can be regarded as the indicator for fault detection and location. The sliding window divides the long original data into multiple short data fragments, and 12 features of SM capacitor voltages are extracted as the feature vectors for the anomaly detection model. Secondly, the open circuit fault diagnosis problem can be transformed into a multivariate Gaussian anomaly detection problem according to the feature extraction of the capacitor voltage of SMs. The model based on multivariate Gaussian distribution is demonstrated to make predictions for anomaly detection and trace back the predicted faulty SM. Then, the fault detection and location method based on multivariate Gaussian distribution is presented to locate the faulty SM. If the probability density of the vector to be diagnosed is greater than the threshold value, it can be determined that the SM is normal. If the probability density is less than the threshold value, the SM is in a fault state. When the open circuit fault is diagnosed for N consecutive cycles, the SM is judged as faulty. Finally, simulation and experiment verify the effectiveness of the proposed FDL method.
In summary, an SM open-circuit FDL method based on multivariate Gaussian distribution is proposed in this paper. After the analysis of the fault characteristics, the voltages of SM are chosen as the indicators of FDL. Sliding window and feature extraction are used to construct the dataset for the ML model. The model based on multivariate Gaussian distribution is trained for anomaly detection. After the fault is detected, it can be located by tracing the corresponding SM number. This method can get high generalization accuracy without extra sensors or an accurate mathematical model of MMC. According to the simulation and experiment results, the effectiveness of the proposed method can be proven by the different evaluation indexes.

Key words： Modular multilevel converter open-circuit fault detection multivariate Gaussian distribution

Received: 10 May 2022

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	Yang Heya
	Xing Wenshuo
	Xiang Xin
	Zhang Wei
	Hu Hongbin

Cite this article:

Yang Heya,Xing Wenshuo,Xiang Xin等. A Sub-Module Open-Circuit Fault Detection and Location Strategy for Modular Multilevel Converters Based on Multivariate Gaussian Distribution[J]. Transactions of China Electrotechnical Society, 2023, 38(10): 2744-2756.

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