An Intelligent Fault Diagnosis Method for Open-Circuit Faults in Power-Electronics Energy Conversion System

Wang, Weicheng; Kou, Lei; Yuan, Qiangqiang; Zhou, Jianhui; Liu, Chuang; Cai, Guowei

doi:10.1109/access.2020.3043796

Cited by 9 publications

(8 citation statements)

References 35 publications

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“…In general, fault detection algorithms for power converters can be categorized as data-driven, signal-processing, and model-based techniques. Data-driven techniques rely on various system measurements to extract fault signatures, thereby implementing fault diagnosis with intelligent algorithms [1]- [7]. In [1], the authors combine the information change in SCADA data with a recurrent neural network (RNN) to compute a residual and adaptive threshold for fault detection in inverters for wind turbines.…”

Section: A Prior Workmentioning

confidence: 99%

Adaptive Fault Detection for Grid-Forming Inverters

Intriago,

Zhang

2024

IEEE Access

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This work introduces a residual-based adaptive threshold for detecting faults in grid-forming inverters in AC microgrids islanded from the main grid. The inverters are modeled as a nonlinear system with one-sided Lipschitz nonlinearities and modeling uncertainties. The adaptive threshold response is evaluated using unwanted events such as busbar and sensor faults. An inequality for the upper bound on the ℓ 2 norm of the residual is derived and used for designing the adaptive threshold. The upper bound is obtained via semidefinite programming with two linear matrix inequality constraints. In the numerical tests, we consider an islanded-mode AC microgrid with two grid-forming inverters, one grid-following inverter, and one synchronous machine with the governor and excitation control. Primary and secondary control layers regulate the operation of the inverters in the microgrid. The performance of our proposed adaptive threshold clearly edges over a fixed threshold method.INDEX TERMS Microgrids, grid-forming inverters, adaptive threshold, fault detection. Nomenclature

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Section: A Prior Workmentioning

confidence: 99%

Adaptive Fault Detection for Grid-Forming Inverters

Intriago,

Zhang

2024

IEEE Access

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“…However, the IGBT open-circuit fault may not be found for a long time, resulting in secondary damage or catastrophic faults of other equipment. Power electronic converters are mainly composed of power semiconductor devices, and the systems are not linear, which limit the application of an open-circuit fault diagnosis method based on a fault mathematical model [166]. The data-driven fault diagnosis method does not need to establish an accurate mathematical model of power electronic converters, where the typical methods include: ANN, time series prediction, SVM, random forests (RFs), PCA, or other AI-based fault diagnosis methods.…”

Section: Monitoring For Power Electronic Convertersmentioning

confidence: 99%

“…With the development of the smart grid, the data-driven fault diagnosis technology of power electronic converters has become a research hotspot in the industry [167][168][169]. Wang et al [166] proposed a knowledge data-based fault diagnosis method for three-phase power electronic energy conversion systems in which the knowledge-based method was used to extract the fault features, and the data-driven method was used to train the fault diagnosis classifier; the fault diagnosis schematic is as shown in Figure 14. Xia et al [167] proposed a data-driven fault diagnosis method for three-phase PWM converters in which the three-phase AC current signals, FFT, and ReliefF algorithm were adopted to extract features, and a sliding-window classification framework was used to improve the diagnosis performance.…”

Section: Monitoring For Power Electronic Convertersmentioning

confidence: 99%

“…Xia et al [167] proposed a data-driven fault diagnosis method for three-phase PWM converters in which the three-phase AC current signals, FFT, and ReliefF algorithm were adopted to extract features, and a sliding-window classification framework was used to improve the diagnosis performance. In [166,167], the influence of diode faults and sensor faults can be considered in future research. Cai et al [168] proposed a Bayesian network-based fault diagnosis method for three-phase inverters in which the FFT was used to extract the signal features from the output line-to-line voltages; a wavelet transform can be considered to realize the signal feature extraction in the future.…”

Section: Monitoring For Power Electronic Convertersmentioning

confidence: 99%

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Review on Monitoring, Operation and Maintenance of Smart Offshore Wind Farms

Kou

Zhang

et al. 2022

Sensors

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In recent years, with the development of wind energy, the number and scale of wind farms have been developing rapidly. Since offshore wind farms have the advantages of stable wind speed, being clean, renewable, non-polluting, and the non-occupation of cultivated land, they have gradually become a new trend in the wind power industry all over the world. The operation and maintenance of offshore wind power has been developing in the direction of digitization and intelligence. It is of great significance to carry out research on the monitoring, operation, and maintenance of offshore wind farms, which will be of benefit for the reduction of the operation and maintenance costs, the improvement of the power generation efficiency, improvement of the stability of offshore wind farm systems, and the building of smart offshore wind farms. This paper will mainly summarize the monitoring, operation, and maintenance of offshore wind farms, with particular focus on the following points: monitoring of “offshore wind power engineering and biological and environment”, the monitoring of power equipment, and the operation and maintenance of smart offshore wind farms. Finally, the future research challenges in relation to the monitoring, operation, and maintenance of smart offshore wind farms are proposed, and the future research directions in this field are explored, especially in marine environment monitoring, weather and climate prediction, intelligent monitoring of power equipment, and digital platforms.

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“…In [10], an intelligent fault diagnosis machine learning method using random forests algorithms for a three-phase power-electronics energy conversion system based on knowledge-based and data-driven methods is developed. The slopes of two current trajectories of three-phase AC currents are adopted to train the fault diagnosis classifier based on a data-driven method (the random forest algorithm), which has the adaptive ability to different loads.…”

Section: Introductionmentioning

confidence: 99%

Twisted Pair Cable Fault Diagnosis via Random Forest Machine Learning

Ghazali¹,

Seman²,

Isa³

et al. 2022

Computers, Materials &Amp; Continua

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Applying the fault diagnosis techniques to twisted pair copper cable is beneficial to improve the stability and reliability of internet access in Digital Subscriber Line (DSL) Access Network System. The network performance depends on the occurrence of cable fault along the copper cable. Currently, most of the telecommunication providers monitor the network performance degradation hence troubleshoot the present of the fault by using commercial test gear on-site, which may be resolved using data analytics and machine learning algorithm. This paper presents a fault diagnosis method for twisted pair cable fault detection based on knowledge-based and data-driven machine learning methods. The DSL Access Network is emulated in the laboratory to accommodate VDSL2 Technology with various types of cable fault along the cable distance between 100 m to 1200 m. Firstly, the line operation parameters and loop line testing parameters are collected and used to analyze. Secondly, the feature transformation, a knowledge-based method, is utilized to pre-process the fault data. Then, the random forests algorithms (RFs), a data-driven method, are adopted to train the fault diagnosis classifier and regression algorithm with the processed fault data. Finally, the proposed fault diagnosis method is used to detect and locate the cable fault in the DSL Access Network System. The results show that the cable fault detection has an accuracy of more than 97%, with less minimum absolute error in cable fault localization of less than 11%. The proposed algorithm may assist the telecommunication service provider to initiate automated cable faults identification and troubleshooting in the DSL Access Network System.

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An Intelligent Fault Diagnosis Method for Open-Circuit Faults in Power-Electronics Energy Conversion System

Cited by 9 publications

References 35 publications

Adaptive Fault Detection for Grid-Forming Inverters

Adaptive Fault Detection for Grid-Forming Inverters

Review on Monitoring, Operation and Maintenance of Smart Offshore Wind Farms

Twisted Pair Cable Fault Diagnosis via Random Forest Machine Learning

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